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Thank you so much. Thank you, ma ‘am. Welcome to all the ladies and gentlemen who have found time to be present here today as we explore the topic of MedTech and AI innovations in public health systems. There’s a lot of AI being branded about here. What we aim to explore during the session is in the public health care and with the three pillars that have been traditionally associated with the public good, public health being a public good. The cost of delivery. That public health care. scales, cost of delivery from the government side and also the cost of public health care for the individual also and what can AI bring in terms of having more cost effectiveness.

The second one will be on the care coordination and how do we ensure that the longitudinal health record get built and the clinicians are better equipped to utilize emerging technologies and AI for better care and the third one is on the efficiency, operational efficiency in terms of how do we ensure that the patient standing the line is treated in the best possible manner and in the lowest possible time with quality being associated. So with these three anchors to public health care, I welcome the panel and let me start with you Saurabh, my counterpart in the government of India. When we talk about population scale deployment of AI systems in health care, how do we ensure that the population is

Thank you, Saurabh. So I would like to inform all of you. Most of you must have also learned about the recent healthcare strategy that has been launched by government of India. It is called SAHI, the Strategy for Artificial Intelligence in Public Health. So as part of that, lots of activities in the field of AI are already happening. So if we see in terms of, we know there is a lot of lack of specialists, even especially in the rural areas. So through these AI techniques, the kind of services that are being provided, whether it is through scanning X -ray images or through diabetic retinopathy, also screening is possible through AI tools. So through that, in the resource constraint settings, we are able to provide good quality healthcare services to the citizens.

We have the eSangevani platform, the teleconsultation where… a person who is there in the PSC, a doctor who is there in the PSC, they can take expert opinions from the tertiary care hospitals. Also, I see artificial intelligence in terms of overall reduction of out -of -pocket expenditure because that is also one of the main important goals of ensuring universal health coverage. So by building more and more such systems, by bringing up trust, safety considerations in the public health system, we are actually also creating public trust in the public health system so that people actually come towards public health system and they rely less on the private health care and thereby reducing the out -of -pocket expenditure.

Similarly, also lots of digitization is happening, lots of records. We have the digital data. And through this digital data also, we can improve upon the overall workflows that are there in the hospitals. We have the resource constraint settings. So in terms of supply chain, management also. So lots of innovations. the health ministry is looking for in terms of ensuring that we will be able to provide the universal health coverage to a person even who is in a remotest of area should get the best quality coverage and also at the least cost. So that is how the strategy of government of India is as far as the adoption of artificial intelligence in healthcare. Thank you.

So you have talked about innovation emerging as a centerpiece in public health and with the strategy of AI adoption in healthcare, the SAI strategy we may look at a UHI movement just like there was a UPI movement where we have the digital public infrastructure in health being set up all the interface layers getting established but that also means bringing a lot of innovation ecosystem to the healthcare. So with the work that you have done Shivji over a lot of time how do you look at the innovation to institutionalization framework in the sense how do we while we every other day there is a health tech startup coming in, how does it get integrated in a structured manner with the public health system.

Thank you and good afternoon everyone. One of the important things which we need to recognize at least in AI is currently solutions are looking for problems not the other way around. Therefore it is important to marry what is the problem which is important for the state and then bring the solution together. So the first step of institutionalization is about how do you apply technologies and who sets the agenda and who is setting the priority. Like the way Andhra government has set up the Center for Applied Technology which has put out a call to say these are the problems we solve for our frontline workers. I think that’s the first step of institutionalization. Second is about the whole although I said solution looking for problem and problem looking for solution these are not either or.

I think both are important. We never knew we all needed a smartphone. But at the same time smartphone has become a problem now. Right in some sense. So I think continuous bridging and very critical element is to taking it to the ground and actually sharing the real evidence because every innovator will want to say their technology is fantastic. Have you come across any innovator who says their technology is not good? All of them will say it’s fantastic, it works the best, it is the best. That’s okay. That’s what an innovator is supposed to do. Whereas I think the state has the responsibility to test it on the ground, to look at the feasibility, to see does it actually change health outcomes.

Does it actually save cost, as sir was mentioning. And the third element of institutionalization, sir, is also the use case library that we need to build. I think there is a lot of discussion around this can do that, that can do this. Where’s the evidence? Where’s the use case library? Where has it worked? Has it worked with tribal communities? Does it work for the last poor woman, tribal leader or ordinary person? Right? That becomes very, very critical. And the last part is around the AI policy. Policy and processes. where the guardrails are built so that the state is also able to have a very clear policy towards how do we share the data, how do we ensure that we are able to, all the data that is shared by the community is actually monetized for them.

Thank you. You started with a great point that most of the time innovators come with solutions and they are looking for problem statements. But in Andhra Pradesh, we have articulated the problem statement clearly in terms of how do you at population scale drive an AI -enabled public healthcare system. And that’s where one of our partners is TataMD, which is represented by Saraswati Imam here. I believe you have set a fantastic stall also on the digital system that you have set up for healthcare delivery. So would you want to talk about your experience and how do you bring up a private sector ecosystem into public health and enable a public healthcare system?

Thank you, sir. As the sir mentioned, I represent TataMD. please visit our stall in hall number 5 where we have showcased what we are doing but I will just explain it in simple terms public health system we are looking at AIS assisting the entire public health system so I will just divide it into 3 or 4 aspects one is for the medical officer how can the medical officer gain from the assistance of AI so what we are looking at is how can the so normally when you go to a PHC the doctor would ask for the vitals to be taken the basic he would ask what is the complaint for which the citizen has come but it tends to be episodic it’s not longitudinal so we are looking at how with the help of AI we can share with the medical officer in a structured manner the entire longitudinal data of the citizen so that the doctor knows this is not just an episodic care we are talking about we are talking about continuum So how can we ensure that we understand the citizens?

So if a NCD patient comes, shows, mentions that he has HbA1c of 8, but has it been the same? Has it come down or is it increasing? So that trend will help the doctor to decide the medication. Or normally they would also ask what is the medicine that you are consuming and they would say either continue or stop. But with the longitudinal data, they will be able to say, okay, is this medicine actually working? Is it not working? How do I ensure that the patient is taken care of better? So I think it helps the data to be structured in a manner in which the doctor can use. Secondly, there are sometimes because of the busyness all of, I mean many of, I don’t know how many of you have actually visited a PHC and seen the workload that a medical officer faces.

Many times they are just rushing through the citizens, right? So they do not have the time. Sometimes they do not have the time. Sometimes they may miss an investigation which is required for a particular. So AI can do that prompt saying that, okay, this is the history, this is the data. Maybe we should get a foot examination done for this diabetic patient. He has not done it for last few days. So basically we are looking at AI as assisting the medical officer with the clinical support system so that nothing is lost, there is no oversight. Plus there is a evidence -based treatment guideline which can be shared with the doctor. And finally, the decision maker is the doctor.

We are not here to say that the AI will decide. The decision maker is doctor. The AI is to assist. So this would be more on the clinical side. Similarly on the operational side, right, all of us know the time that is spent in detailing out what the conversation with the patient is. So we are looking at how that can be made in a meaningful manner in a rural public health system. We all know in a closed room probably the listening can be better, the ambient listening can be easier managed. But in a closed room, we are not here to say that the AI will decide. We are looking at different dialects. We are looking at different dialects.

We are looking at different dialects. We are looking at different dialects. different contexts, how can we make that better? So that’s the second part for the clinician. Looking at the frontline workers, I think some of the AI bots, we are looking at how we can help them with their tasks. So if an ASHA is looking at 50 pregnant mothers, how can she prioritize who is the one she needs to look at, who is the high -risk mother whom she needs to prioritize? Because all of them are loaded with work, but AI can help them in scheduling their tasks, do their tasks in a better manner. And lastly, if I had to look at it from a public health system, the government, public, the health department, we are looking at AIs, how can it provide the analysis in a manner that it makes better sense for the government to see the trends?

How can the data show to them that, this is the… key problem in this particular area. We’ve been talking about with Andhra Pradesh government on creating wellness course, composite wellness course which looks at patients, looks at environment, creates a score which can tell them how to look at where the problems are and provide solutions. So basically this is going to strengthen the health department in identifying the risks, predicting the risks and looking at ways to do a proactive preventive care. So this is the way we are looking at ensuring that AI is providing support to all the stakeholders by using the data that is being provided and there’s a lot of deeper work while what I say is on the surface, the deeper work is how do you understand the data, how do you capture data across different geographies.

So that it’s more meaningful and it’s

I’ll come back to you in terms of the challenges you face while working with government, especially looking at the fact that government is probably implementing 25 odd public health care programs and the flavor is from preventive health care to maternal child health to genetic care and so on and so forth. But bringing a different stakeholder into the conversation, Mr. Seth, you represent a social impact organization and have been working on tobacco control. So where do you think is the maximum value of AI in health care driven from your perspective while you’ve heard the other people talk about digital platforms and enablements and innovation? What in your mind will be the biggest AI value generation for public health?

Thank you. Thank you, Dr. Mr. Gaur. You know, large public health programs, TB programs, prevention, tobacco control. adolescent health, the real question is where AI can actually help them day to day rather than in theory. And if you see most of these programs across states, the failure is not because of the design, because they’re not reviewed, but because of variable implementation across areas. Now, the data exists, reviews are also done, dashboards exist, but you find we very often find out what’s going wrong after the event when the failure has already occurred. I have heard so many senior level IS officers lament, the dashboards only tell me what I have not done. They don’t tell me what I am supposed to do.

Now, that is where I think AI can come in and add a huge amount of value. By, you know, helping and telling you where the failure is likely to occur. Identify where it is happening. Who has to take action on it? And then that action, I mean, you know, the person can be informed. And then, of course, you know, where the action is, that’s so important to pushing it. But for this, AI has to exist inside the delivery system, not on top of it. So, in my mind, that is where AI fits into the delivery system.

Typically, a PHC, a Primary Health Care Center… that supposed to see around 40 patients or doctor ends up seeing 60 patients, at least that is the statistics for Andhra Pradesh. You have all been to institution like AIMS where heavily what do I say, the fact that there is no care coordination or absence of care coordination, so everybody seems to be ending up in a tertiary healthcare setup. And there representing a tertiary care unit Dr. Rakesh and coming from AIG hospital, how do you augment, how do you do clinical augmentation for the doctors and in private healthcare, what are the lessons that have been learned and can be adopted in public healthcare also? Dr.

Thanks, Saurabh. In fact, I would start by saying, A is going to reduce the cost both in public and private health not by replacing the doctors but for the early diagnosis and intelligent triage. See, for example, as my co -speaker has said, this is a 1 .4 billion country. And as of now, I think it is going to be around $20 billion. And I think it is going to be around $20 billion. And I think it is going to be around $20 billion. And I think it is going to be around $20 billion. I mean, it’s growing day by day. So any hospital, it can be a primary, secondary, or tertiary care hospital, has got a huge volume of input. And it’s very difficult for anybody, even a robot also cannot match the human scale.

So these need -based innovations are something which we really have to look upon. See, suppose I, in my hospital, have got something where I have difficulty in doing it, and in other hospitals, something else. So need -based innovation we have to catch and then try to solve it. For example, in private setup, as I told you, there are some use cases which we had a personal experience since the last three, four years. I’ll tell you a little bit of economics on this. There is an algorithm which we developed with a pure AI model, costing 500 rupees to pick up a fatty liver, versus getting a machine which is 1 .2 crore and charging 5 ,000 rupees per piece. So this is something which is a need -based innovation for me as a gastroenterologist.

I’m a hardcore clinician, and I look upon any metabolic disorder which is the crux of the entire metabolism. And if you have tools like this, that will give you a lot of value in terms of fast diagnosis as well as the economics getting scaled down at your level. Then there are other use cases where you have the EMR and ESR. In fact, Sarabhiji, there’s a lot of chaos which happens when you have the admissions in hospital. So in that, we have a use case where the patient, they stand there, and the discharge summaries will take 8 to 10 hours for them to come out of the hospital. So we have an AI -enabled system where the discharge summary will happen in half an hour max once I say my patient has been discharged.

Vis -a -vis when you have electronic medical records and you want to have the patient bed management made. so one patient there’s a huge line where you start it’s all a personal experience in this hall everybody goes and you’ll be standing in the queue and even it’s not that you have to blame the hospital authorities but again that is something where in those areas you need these AI enabled systems so it can be digital health or a clinical related AI system so that’s where we have to concentrate on

so while you do that my question is again to you only go around the panel in a reverse order now and so while you do that and look at private sector efficiencies being coming out how do you think you can collaborate with state governments or governments at large in terms of the fact that you will be an early deployers of medtech solutions and the fact that you will have built it in your cost economics to use them faster how do you think you can accelerate their adoption in government ecosystem also

it’s a very valid point in fact the the see as a private sector person we have the early adaptation and adoption compared to getting into the public but in fact on that note i would say i couldn’t bring the aim foundation which we have which is working closely with the government of anupadhyay and other governments so what we did is we formed a platform with triple it hyderabad indian school of business iat delhi the fit and it is a neutral platform where anybody can come and then pitch their idea we handhold them nurture them and then make it validated at our clinical level and once we have the products for example the journey mitra which is launched in the government of anupadesh as the co -speaker told so that is something the asha workers can pick up with the a enabled system about the high risk pregnant mothers and then the nutritive value to decrease imr mmr so tools like this which we can do at our level and once we validate and we feel confident then we can give it to the public systems so there should be a public private integration, which is the main strength for this country.

And then only this will scale fast because time is running fast and nobody waits for us. And we have to keep up with that and then try to get the solutions because we cannot adopt the Western world solutions to us. Ours is entirely a different system. So we can never take any Western AI algorithm and then try to adapt. We have to have our own algorithms and we can do fast because of the population we have, the volumes we have, and of course the zeal we have.

That’s great. In fact, we are working with the AIM Foundation and then looking at setting up a biodesign lab in Andhra Pradesh with the AIM Foundation, with all the other institutes that you’ve talked about. And I see a lot of facilitation of deployment of Meta -X solutions happening through the CAT, the Committee on Advanced Technology. And the biodesign lab. But while we talk about all these Meta -X solutions, the core is something that… we believe as a state also that preventive health care has to be strengthened. And that’s where for prevention as an entry point, where do you see AI playing a role in terms of preventive health care being strengthened, Sanjay ji?

So I think prevention programs, as we all agree, prevention is better than cure and preventive programs will have the highest ROI. Unfortunately, preventive programs are not politically supported. Right. And that is where AI, I mean, if you take adolescent health, you know, student health, nutrition, you know, and I mean, if you take non -communicable diseases, we are talking about behavior change across entire populations, and that’s become the most important, you know, today maximum number of deaths are taking place is because of NCDs. That’s where preventive. So, you know, health comes in. Now where AI can really, I mean why AI really fits into this, because these programs operate at scale. They require continuous and repetitive activities to be done.

And they also show very predictable gaps during implementation. Okay, where the drop -offs are taking place, where the failures are taking place, and actually, you know, prevention, or sorry, the program implementation being done. And the number of variables are also very large, because as soon as you talk of behavior change, you are talking about, you know, huge amount of different cultures taking place. Different cultures respond differently to behavior. And if you take the mass of data, this is where AI can really support the programs and bring the, you know, not just the cost down, but the effectiveness of the programs to take place. But as I said earlier, AI needs. You know, I’m going to do this, and I’m going to do that.

And I’m going to do that. be within the delivery thing, not as a layer on top. And if you then, if we focus on, you know, how these schemes or programs result in outcomes, all right, this is where I feel that AI can give a very vast feedback. All these different entities, facilities, units are feeding a huge amount of program data coming in. AI can analyze where the likely failure rates are. It can escalate it to the appropriate level, bring it to the attention of the senior people, and that will result in far, far better delivery outcomes.

So can you be more specific in terms of, for example, the tobacco control program that you run, Sanjay ji? Right. Where is it, do you think that the fact that if we are doing it across, say, 20 ,000 schools and I do not know how many schools you are doing it with, are you able to do those kind of predictive outcomes? Outcomes or prediction in terms of where the program is. is probably bound to fail or is looking at a failure condition and the actions that need to be taken.

Oh, yes, we are getting, we are running tobacco control program in Antara, as you know, more than 20 ,000 schools. And each school is supposed to do a standard set of nine activities. All right. So very early, we are able to see which, you know, schools are not doing some certain activities. All right. And if we manually, if we start analyzing across 20 ,000 schools, there’s no way we can do it. All right. So AI helps us to analyze the data and say this block, this district, this area, there is a failure taking place. These schools are not taking action. All right. And then when we in terms of after the analysis, what we are doing is also the schools which are acting when they do the activities, they upload the activities.

Now we do image recognition. And decide whether the activity is done correctly or not. And we’re very high, 98%. accuracy we are able to see whether the activity has been done correctly or not and that tables enables us to give feedback immediately look within I mean as soon as he enters it within shortly after that he gets feedback you haven’t done this properly please repeat it all right then once we are talking about you know informing people so we are now sending out in Andhra Pradesh for instance 40 ,000 teachers get messages from us personalized messages you know for each person in the language he prefers in the language you know in the tone he prefers and that makes the motivation or the you know the way they act much faster than they used to earlier so we are seeing orders of magnitude improvement in terms of effectiveness of the program taking place I believe is you know works for I mean not in Andhra but in other states we are seeing this same thing happening across other programs which we have been working on

This is very heartening to see. So while for example you may be doing a tobacco control program with us in Andhra Pradesh, there is a cancer care program happening in Tamil Nadu that we got exposed to in one of the workshops. There are other states doing fantastic work. I saw Odisha for example, the stall today. So while and I probably picture to you Shivkumar ji that while we have all these islands of excellence and innovations, what is it that prevents them from scaling up and what are the structural barriers that probably government is not able to while we talk about ease of doing business, what is that ease of doing governance, public governance and public health care system that will make them scale up?

in place and the data quality actually improves. AI models really can’t work on top of it. And there is an exception in terms of, you know, you’re doing surveys and various other data points are there. Most states don’t have it. Right? And therefore, the processes unless they throw data out, I think we can all dream about AI, but really having the kind of value that we are talking about is going to be very, very difficult.

Thank you. Thank you. That’s a great point. And while we are at a certain maturity level in state government of Andhra Pradesh, there are other states which do equally well and there are states which probably are lagging. But with the national framework being put and with the ISHMA and Bharat Digital Mission, I bring it to Saurabh, my colleague. Where do you think in government of India, how do you facilitate all state governments to at least come on par and how do you see AI within the national health systems which become say gold standards or standards at least for all the other states to follow?

So as we know that health is a state subject, so ultimately government of India works in collaboration with the state government. And we understand that the kind of AI systems, the algorithms, the applications that have been developed for AI, ultimately the quality of output that comes out from those systems depends upon the data on which it is trained. And that is why it is very, very important that the data should be representative. It should be from every region because every region has a different kind of disease profile, every kind of various kind of demographic profiles. So this is very, very important that the data should be representative. Data quality, as was mentioned, should be very good.

And in fact, through this Aishwarya Bharat Digital Mission with more and more of digitization, we are and with every person now being provided with the ABAID, which is a… Actually, an ID which is linked with the health record so that the health records can move. with the person. So with all this digitization, with all the data that is being generated, we are able, we can do lots of usage of AI in terms of disease surveillance. We can use it for modeling of various diseases. We can use it for imaging. Lots of MRI because as I have mentioned earlier, still there is a lot of issue about the availability of specialist doctors, especially in the rural settings.

So at least if the AI solutions are available, the basic, at least 90 % of the imaging can be taken care of by the AI. So that only the most suspected cases can be referred to the tertiary care hospitals and the basic healthcare can be managed at the facility level. And as my colleagues have also mentioned, one of the issues in public health delivery is totally preoccupied with lots of administrative work. Lots of data entry, lots of portals that they have to enter data into the portals. That takes a lot of time beyond what is expected out of them, which is their clinical duties. So with this more and more, the AI application and more and more systems getting digitized, we can have a system where the data which is fed into one portal can be automatically populated all across the portals and the administrative work of these healthcare workers, which are our frontline healthcare workers, can be substantially reduced so that they can focus more and more upon the actual clinical work.

So AI is ultimately, it’s about improving efficiencies. It’s about improving the workflows. We have the supply chain management also. It is about optimizing of supply chain management. And in this entire journey, in the adoption of AI, we take states as our partners. Because ultimately, when both government of India and states work together, only then we can have a very robust AI system which can actually deliver quality care to our people.

We are here to work with the government of India very closely and establish those models. But the point you made, Kher, I actually cannot think of working as a NM myself also, despite being, adding a state health department. The sheer fact that a poor NM or MPHA male, the multi -purpose health assistant or the nurse on the field has to work with 25 programs. And while there is this national architecture coming up, there is so much of digital literacy challenge, not just digital literacy but adoption and using all these apps. This is a real challenge we face at the state level also. And with TATA, when we are building the digital backbone through project Sanjeevani that we are doing together in a collaborative approach, in an example of public -private partnership, I would want, Saraswati, you to play the devil’s advocate role and tell us the three key technology integration challenges that you see.

And please be critical of the system. But tell us that what is it that you would want to see. what are the challenges that you face day in day out when you look at building this care coordination oriented digital backbone for public health in Andhra Pradesh

Thank you sir, tough question to answer especially in a public forum but I will do my best, so one of the things like we have spoken about in a PHC there are lot of things to be done and like you mentioned sir, all of them have a lot of activities lot of programs, lot of reporting that they are doing introducing AI as something like Sanjay ji said as something additional or bringing technology as something outside is definitely a challenge, so our aim and what we have realized is if it is not integrated in their workflow, if it is not something that they find value in, the adoption is going to be a challenge like Shukumar ji mentioned that people are collecting data and just sending data But is that data really helping them?

Is it helping the people who are collecting the data? Is it helping them to do their work better? Are they able to benefit from the work that they are doing? If the answer is no, definitely they will not take it up. So one of the things is how do we integrate in the workflow? And they find value for what is being introduced. The moment that we are able to reach that sweet spot, I think they will start utilizing it. So one is how it’s integrated in the workflow. Second, I think, is it’s less of technology management and more of change management. Whenever there is something introduced, people look at it. I mean, there’s a cycle of adoption similar to what even we all face whenever we get introduced to anything new.

There will be a set of people who are ready to adopt it and are forthcoming. Then there will be a lot of people who are resisting. Slowly, the pattern changes. And people start seeing benefits. So what we are trying to… get at is who are those people who are seeing value for it who are those early adopters how can they bring in and probably with them we train the model like shikumaji rightly said if we do not train the model correctly you’re not going to get the good benefits so who are those people who can be utilized to train the model and who will not resist and then you give the trained model to people who are resisting so that they can see value so i think it’s a lot of change management related resistance which is what we need to address and lastly while i mean andhra is a very progressive state and we see this not as a challenge here but generally the connectivity the power availability all these tend to be one of the other challenges that for doing it a system wide change how these could probably be the this as i said in andhra thankfully those are not issues that we have seen but finally i think it’s about the incentives, right?

What are the incentives for people to adopt? If there are incentives for them to adopt both from the state side and from their personal side, the adoption tends to be easy. So, it’s a lot of work that we need to do to make sure that this is taken at scale, sir. Thank you.

So, I think we have a round time for one more quick round and I want to keep it short. Thinking aloud, what do you think? And the question is to all of you in the panel. What is the one maximum impact zone or maximum impact innovation that you feel based on your engagement with public health or with healthcare that should be happening? And I start with Dr. Akesh. What do you think will be the one most impactful thing that we can do? That’s a

very difficult question. There are many things to do. But what I would say in a nutshell is with the current scenario where we are, so the clinical insights from the doctors, the engineering capability from the bioengineers or the AI engineers, and the policy support from people like you, so this is something which will make the AI -related or metric -related innovation to go from the lab to lives. So that should be a collective holistic approach which we have to do and join hands together. I would say in that way that’s the need of the hour. Thank you.

I’ll make it simpler for you, Sanjayji. In preventive healthcare, which is one most impactful innovation that should happen?

Since I’m working in that area, I guess that is where I will obviously state, but if you look at it, Andhra Pradesh, 48 ,000 deaths every year because of tobacco usage. If you take any of the adolescent health, the future of our youth is how well our adults are. lessons grow. As I said, preventive is the highest return on investment, and it is not glamorous. It is very dull. It requires enormous amount of day -in, day -out discipline. But as a state, if you’re looking at what can really give you the maximum amount of benefit, I’d argue for preventive health. Thank you so much.

To you, Saraswati ji, in terms of engaging, and in the public -private partnership board, which is the most impactful thing that can be done?

I would probably respond slightly differently. I think in terms of bringing back the trust in the public health system, that would probably be the focus, and that hinges on quality of care that we are able to provide in the primary care, and that is what is going to ensure that the need for tertiary, secondary, and the disease burden that we are envisaging, that would probably… probably be managed if we strengthen the primary care with the trust in the public primary care. Thank you.

And Shivji, with you heading our committee on advanced technologies and doing all the work with innovation ecosystem, which do you think is the most standout innovation that you have seen that can be impactful for public health?

Sir, I’m going to be a little controversial on this. I think technology is just an enabler. I think our single biggest problem is going to be work culture. Nature. Work culture, incentives, and today every officer feels that they need to see a dashboard and tell their team what to do. I think if we have to really make AI help everybody decide, I think the work culture around evidence, the work culture around data is going to be the biggest one. But I will answer your question. The biggest innovation should be people’s data should be owned by data cooperatives. Nellore is a district. In Andhra, Nellore people should own the data through a data cooperative. and we should have reverse tokens where people pay for their data.

And we are feeding the AI engines and I think our people should gain from that. When we reverse that, sir, and when we reverse the incentives and the work culture of use of data, I think automatically you will find people coming and telling you this is how I am using it. Thank you.

That’s very interesting. And what about you? What do you think that can be the most impactful at a national scale also for the health innovation that can be there?

I would just also like to address the work culture issue that you have mentioned. In fact, if we can just sensitize, if we can make our doctors, our health workers confident that the outcomes are predictable, outcomes from the AI systems are good. And actually by adoption of AI systems, their productivity is improving. The kind of work that they have to do in less number of hours, in less time, they are able to use that. Use this. Use the same. Do the same kind of work. And they can do better in terms of their clinical approach and their productivity approach. I think. I think our health workers, they have adopted very swiftly to the technology. And if we can show them the reliability, the outcome that is certain, and overall improvement in their productivity, definitely workforce will adapt to this technology.

And as far as coming to your question, I think diagnostics will play a very, very important role in terms of the adoption of AI. And we are seeing it in the area of tuberculosis and also in diabetic retinopathy, where through the scanning of these images, the doctors can make a very evidence -based decision in a very less time. So in the same time, if they were seeing 10 patients, now with the support of AI, they can see 20 patients or 30 patients with much more accuracy. So the kind of shortage of doctors we have and the kind of patient load we have, especially in the tertiary. I think diagnostics will be playing a very important role. huge role in the field of AI.

Thank you.

I think that’s all the time we have. We still have time for one or two questions from the audience. If somebody would want to, the gentleman at the back actually raised his hand first or I spotted him first. There’s a mic behind you.

For mental health perspective. Because that requires additional safety, security as well as sensitivity. But I have not seen anyone touching on yesterday also as well as today. Mostly we talk about medical imaging and that takes because radiology as well as radiology all the innovation. there are developments also but I was thinking I will get some insight but so far

Dr. Rakesh you want to take it?

I think I have a point on that it’s a very nice question so on the mental health there are people in the western world who have got some apps and they are doing it but in India unfortunately there is no robust system to collect the data in fact if you have suppose you are working in a private hospital you have a robust EMR, EHR then you must be having a questionnaire on which you can build up these things but that uniformity to come it takes a little more evolution but there are people who are working on it in Indian sector probably it will take a little more time in fact you will be the one who can start that at your level

no we are working actually we are struggling I am at Eames Bhopal and what we have audio recording like we don’t have medical imaging either you have mental status examinations or video recording and based on that voice recording similar like detection of suicidal ideations, detection of depressions, anxiety and those kind of things. So there I was seeking if some assistance or guidance can

So I will probably just respond to this. What we have done in Andhra Pradesh is we have worked with psychiatrists and so there is a methodology called QPR, question, persuade, refer, which is actually proven in the sense that it is patented, where we worked with them and said that okay, especially with let’s say our students who go through high pressure, people who are in intermediate education, 11th and 12th, and there is pressure to perform an examination, parents are pushing them. So out of those 10 lakh students who are appearing for examination, which are those and our estimate is say around 15 % need to have special focus being paid. So taking all of them through this QPR methodology, working with this organization called Suicide Prevention Foundation of India, SPFI, and working with them, we have been able to at least look at which are those students who need to be given specific focus who are having those kind of ideations or having those kind of vulnerabilities and what kind of messaging needs to go for them and while it is a challenge and I am not saying there is a lot of AI into that because there is lot of what do I say privacy issue also associated with this but the other point you said about a scribe or essentially since people are talking you can actually get into the behavioral insight and understand whether what kind of ideation is happening and getting answers out of that I think that is a great point and would love to work with any innovator who would want to do it as a sandbox with us.

Thank you. One more question. Yeah. No, no, we will go to. You are an in -house person. Yeah, please go ahead.

So first of all thank you, Saurabh Gaur, sir. The first thing about that MedTech challenge I think this is the first time we have seen state government or government is opening up and telling that why don’t you guys innovate as come with your solutions including small startups like us. and we will give you a platform to pilot it and then finally help us in scaling up. My question more is to Saurabh Jain sir because we need to replicate something like this at a central government level. At the start -up, we definitely cannot go to all states and keep on doing pilot while MedTech is a segment where almost zero VC or private investment is there.

So it’s largely we running on either government grants or our own save money or loan or everything. So is it central government can create a platform which Saurabh Gaur sir or other state government can take those validated solutions and scale up them and we don’t just keep on repeating the same thing?

I think yes. ICMR is developing this kind of sandbox in which the start -ups can come up with their innovations. You can test it in the sandbox. So ICMR is actually developing this kind of mechanism to test the models. And ultimately, it’s about replication as you have mentioned. So once it is tested and it is tested across various settings depending upon the… outputs… Definitely it can be scaled up.

Thank you so much. The audience, you deserve a round of applause for being very patient audience. And I thank all the panelists also for their very valuable insights. Thank you. Thank you, everyone. There is a moment to give also. We can quickly hand over the moment to Dursu. Thank you. Thank you.

healthcare, finance, education, urban planning, but I’m going to only focus for a few for this particular evening. First, access is a multi -layered problem. Good technology by itself does not bring in or include people. By adding AI, you’re automatically not going to include more. The last mile gap is still a problem. You need to be able to focus on connectivity, in skilling, in the interfaces that people use. You must take into account the needs and wants of multiple communities. One of the other key observations that was important for this was understanding the power of the purple economy. The market of assistive tech products for people of persons with disabilities and people with special needs. These are often perceived to be on the margins of our reality, but they are not.

As one of the largest populations of people with disabilities in India, India alone has the potential of 150%. We have $150 billion just in this space. These are people who can purchase. These are people who can access these products. We need to be building for them. It’s not a charitable cause. It’s a simple business proposition. Second, a lot of AI products are stuck in the AI in the pilot stage. You often have a great idea, but you’re not able to execute them. These are for a lot of reasons, but fundamentally, they’re usually around the surrounding system. Like I mentioned, last -mile diffusion, funding, or limited support to be able to scale them up. Third, and this is something you have seen across the summit, language is foundational for enabling inclusion.

Whether it is a banking system which is using a voice AI for credit facilities or an educational AI tutor which you made for a rural village in India, all of them require to be understood in that local context where it’s operating. And this is something you would have seen across the summit in various. Exhibition halls over the past few days. And the last one is institutional capacity. this is a break or it can make a variable as well what you’re going to see is a lot of governments need to build technical expertise in the space of AI we need departments to understand this further this is already happening and once you see this you will see this reflected in procurement standards in technical specifications that these departments are making and this will lead to increasing adoption as a result of these findings then what do we have to suggest at the report there are three interconnected pillars like I mentioned in the beginning design, access and investment anything around AI and inclusion needs to take this into account first, looking at design you need to ensure that you’re embedding inclusion from the start a lot of AI systems are shaped very early and at that stage our recommendation is to have participatory design involve the people as you’re building it out if you’re making something for ASHA workers and you don’t involve them that happens that product is bound to fail in the last minute access.

This is where you have to make sure AI is usable in real world conditions. I know we’re in the AI Impact Summit, but something which you need to know is at least 33 % of the world, that’s 2 .6 billion people, still don’t have access to the internet. So when you’re thinking about building AI tools, you need to take into account those real world contexts, low bandwidth environments, not everyone has high speed internet or a full fledged smartphone. The third is investment. We need to align procurement, capital and incentives. Governments here can play a crucial role by acting as anchor buyers for these kind of products. By embedding standards which reward accessibility and open standards, you will be able to shape market incentives.

Creating these incentives we believe is very important to be able to scale inclusion through AI deployments. The last part of our report, and this is something which is my favorite, are these use cases. And And our report documents a bunch of them. Over the past few days, you would have seen a lot more than we could even account for. I’m just going to focus on two of them, two, three of them, which I really like. One is Shishumapin from Badbani AI. This is a very small tool which allows ASHA workers, frontline community healthcare workers, to take a photo or a video of a newborn baby and get accurate measurements. And this is very important and this is a very simple tool.

It can be used with low internet and can be used offline as well. Second, and you will hear from Augustia soon, I really like the reban glasses. I even tried them out at the Meta stall here. The Be My Eyes feature of that is something which a lot of people with visual impairment are using across the world. Something which helps them navigate the world around them. This is something which Meta has built by involving these people in their design process, involving them as they took decisions. And lastly, this is a shout out to the YesSense. To access app, you may have seen them in installs here. This is a very interesting tool where… you go around, take photos of buildings and physical spaces and understand whether they can be accessed by people with disabilities or not, creating a database which then allows for future greater policymaking in the future.

The crucial thing to note in all of these use cases is that all of these products and tools follow the principles which I talked to you about. They look at design, they have been supported by different government departments and finally they are looking at low resource context environments to be deployed. I am sure at the end of these five days we know that AI is going to expand access and opportunity. The question or doubt really isn’t that. It’s whether ecosystems will now choose to build systems that are required to make this expansion durable, equitable and sustainable. Our report will be out online soon. Thanks. Thanks so much.

Thank you so much Nirmal for those insightful findings. May I request? Now everyone at the panel to please come for a photograph. this is the launch of the report as well so we’ll just take a quick photograph up so if you could come ahead with the report up front Nirmal please the project team who worked on it Yes Thank you very much We’re now going to move to a very interesting part of the event which is hearing from people who actually build these products. To take us through that we have Rutija Paul who’s a partner at Ikigai Law at the panel Rutija over to you.

Thanks Rahil and thank you Nirmal for that wonderful presentation and to the audience for staying back for so long on a Friday evening. So thank you so much. Panelists, incredibly grateful for your time. I know it’s been a very hectic week for all of you. So thank you for taking out the time. And I think Nirmal, he set up a really good context about the three things that we thought were important from our findings. Design, access, and investment. And how do we sort of, you know, use them interchangeably and together to ensure that inclusion is not just a concept but really becomes, you know, really common in the conversations and all of our products. So I’ll start with actually Aragya.

Help us understand how has your product, tell us first about your product and how did you go about designing it, but also how has it enabled access to justice in a country as big as India and all of the issues that it has in the justice system.

Yeah, sure. Firstly, thank you so much for having me here. I’ll probably start by painting a picture of a district court. A lot of you, I’m sure, have been to a district court. by virtue of your profession, but there’s towers and towers of paper everywhere. I’m not a lawyer. The first time I went there, that was the most surprising thing for me. I saw more people writing with typewriters and not computers. And then there were people spending a lot more time looking for the right files than actually going through them and understanding what’s written in them, right? And so when you look at all of these things, it becomes quite clear that justice in these settings is really not a question of law.

It’s become a question of logistics. And that’s where Adalat AI comes in. We build AI and technology to make courts more efficient at a daily and weekly level. And the hope is that when you do this at scale, you can affect the case pendency problem in a rather positive manner. Now, coming to your question of how does AI actually enable access, I think what we are seeing is that there are two tracks. One is the more direct track, and then there is the indirect track as well. When it comes to the direct track, which is how does it enable communities to access justice better, I think there is a huge information darkness problem in the country.

It’s very hard to access judicial information about your cases. If you are in one, what’s going on with it? When is your next date of hearing? And there’s always multiple layers of middlemen that you need to sort of go through to access justice. I think the one use case of AI which we feel is quite safe now is to access information easily. And to that extent, at Adalat AI, we’ve built a WhatsApp chatbot which any citizen can access. They can talk to it in any language that they want. You can just give your name and your PIN code and it’s going to tell you if you have a case. And if you do have a case, what’s going on with it?

When is your next date of hearing? What happened in the previous order? This is not suggestive by any manner. In fact, we discourage any sort of legal advice using AI models at this point. I don’t think. That’s the right use. This is more around. given the information that already exists in the systems behind rather broken, you know, sort of websites can be kind of sort of bridge the last mile access. The more indirect sort of opportunity is by making the institutions of justice be more efficient, which is what we do with our core judicial product. We try to make courts more productive, you know, so writing everything down by hand. And in a courtroom is a big pain point.

Ninety percent of India’s courts don’t have stenographers. So we built a legal transcription tool, which is multilingual. You could understand the legal jargon that lawyers love to use, like rest your decata and whatnot. I’m not exactly a lawyer. It understands Indian accents and dialects. And what we are seeing is that courts that do use technology like this are able to improve judicial productivity two to three X. So if someone was recording two witness depositions per day, now they’re able to record four. to six. Now, when you do this at scale, you can get a lot more done at a daily, weekly level and then hopefully that helps the case pendency problem. We’re also sort of tackling a lot of other different judicial tasks like going through thousands and thousands of pages.

Can we help them navigate it? Can we digitize the entire workflow so that you don’t have to go through a lot of bundles of paper? What we are steering away from at this point is anything that involves legal intelligence. For example, something as simple as summarization too. We don’t think it’s safe enough right now because the summary for a citizen looks very different from the summary that you need for a judge versus a summary for a lawyer. And so that’s something that I would advise everyone to tread with caution on.

Alright, that’s interesting. Thanks. I’m going to come back to you on the aspect of what has been safest to access information. But, Olivier, I wanted to come to you. next. One, very curious to know about Rwanda’s AI scaling hub. And second, Kinyarwanda, if I’m pronouncing it rightly, it’s your go -to language, right? But it’s also a very low resource language. So when you look at using an AI tool based on that language, how has it been? Has it been incredibly difficult? What have been your learnings? And just everything about the hub, please.

Thank you. I hope everyone can hear me. And thank you, first, for having me here. And I’m happy to share. So, as she said, I come from the Rwanda AI scaling hub. And you wonder, she asked me a question when we were out there. She said, why the scaling hub and not just the AI hub? But the whole idea is we, as Rwanda, took the approach of thinking of working on solutions that can be scattered. so that we do not end up just having pilots and we stay in pilot mode, if I can say. So in that case, the AI Scaling Hub has one main mission that has two key pillars. And the mission is really to drive the AI implementation while ensuring that those implementations are aligned with the national priorities for socioeconomic development.

We focus on mainly AI solutions. And then we have two pillars. One is to encourage or accelerate the adoption by basically looking, scanning the world, and find those use cases. Those solutions that have succeeded elsewhere. and see which one inspires that should be brought to Rwanda, adapted to the context of the country, and then implemented to be scared and do the impact in the society. That’s one pillar. The other pillar is now build the ecosystem all around it to make sure that, one, those implementations can be scared and sustained. Two, they open up the door of possibility to actually be able to, I would say, create much more than this. That basically the ecosystem of innovators and all the other institutions and key stakeholders that really needs to make sure that this movement does not stop.

So that’s because we look at AI as, you know, Rwanda as a country have taken the direction of making sure that the country becomes… African hub for AI research and innovation. So that requires now to really go into this thing, and we are the scaling hub because we are also powered to really move as fast as possible in order to show the impact. So that’s in summary what we do, and we have three key sectors that we focus on, but we are not limited on this. Since we talk about the ecosystem, we really drive this whole thing as much as possible in a very agile way. We are the startup -ish type of institution. If I can say it like this, we find a way to make things happen.

So that’s why. And now talking to King Aruanda when it comes to AI solutions, there is something that in India many people may find or take for granted. But which is not somewhere everywhere. when the AI revolution started India had mature DPI which means that the focus has been more to actually implementing the AI already on existing and mature and trusted DPI that are in place it’s not a scenario in many places the Rwandan approach is actually building the plane as we fly it there is a lot of advancement into DPI I would say if I look at it from a technical standpoint everything is at least at 80 % but not necessarily at 100 % it’s more of plugging into things as we go, the DPI stack is being completed but the AI also needs to take off and go into this so there comes basically with that approach that’s why looking at it holistically is key and when it comes to Kinyarwanda definitely Rwanda is a small country compared to India in terms of size and in terms of people but it’s also a country with a high density population when you look at the way it is and the entire population speak one language which is Kinyarwanda as one of the languages that other we speak, basically which means that actually a solution for it to be adopted, it needs to be speaking Kinyarwanda and AI did not originate in Rwanda so AI does not speak Kinyarwanda originally so as we build our plane, there is the time of also now building the models, building the data set for the language be it the text be it the voice in order to get to perfection so we are doing this as we go and there is improvement every day.

I think that a couple of years from now, we have, I would say, a full stack data set of Kinyarwanda language that can now operate all this. But even right now, we are doing things. That’s the approach.

That’s very fascinating. I think building a plane as you fly is going to stay with me. Thank you for that. I’m going to come to Archana next. I’m just going to pivot a little to a B2B conversation. You help businesses across the spectrum, be it healthcare, BFSI, education, scale up and transform digitally. What does access and inclusion mean in these rooms? How is it that you really convince your clients that inclusion and even access needs to be really embedded in the first thought of your transformation journey?

Thanks for that question, Rituja, and thanks for having me here on this panel. I’m going to take three examples. Recent ones. The first example, we were working with a humanitarian agency which deals with refugee crisis. So they had approached us to develop an AI solution for the field workers who operate on the field when a refugee crisis is happening to look at real -time where should the aid go. Because when refugee crisis happens, assume a blast happens, something happens, there’s a lot of aid that flows in. But is it reaching the right places? For that, you need to process real -time information. For that, you need to look at what is happening there on the ground, which you could be getting bits and pieces from the representatives who are there.

You need to be able to access information that’s flowing around the media. So there’s a lot of data crunching intelligence that needs to be baked in. And typically before AI, a lot of this was relied on telephone calls. That’s manually done. with AI this is something which helps but in this kind of situation most of the time your internet doesn’t work most of the time the connectivities are down because in this situation the connections go away and your AI still has to work you cannot say that I don’t know where to give the aid because my cloud connection went down or my net didn’t work or the connection was down by the government at that point in time so when you design an AI system like this you need to be able to figure out what needs to work offline what should work online where to bring in how to architect it and that becomes crucial so that’s first example where AI needs to be accessible inclusive by design I’ll take a second example so second example a global bank one of the largest bank in the world approached us and their request was, hey, I have a lot of financial literacy videos on my website.

Typically, those are in English and from an accessibility standpoint, there are some captions in English which come in but those don’t necessarily serve hearing impaired because for them, their first sign language, first language is sign language, not English. What can AI do here, right? So the question was, can we use for a little bit technical terms like the vision LLMs and some of the processes that are out there, technology, to create videos which probably were not accessible initially to a large set of population and make it accessible. So again here, something existed but you are using AI to put and add a wrapper on top of it. of it. So you are not accessible by design in this case, but you are trying to use AI to make it accessible.

Whereas in the first case it was accessible by design. And let me take a third case, which I was getting into quite a bit of heated conversation with the CTO of that insurance company, where they did a small POC with AI, where it was a conversational thing. Somebody calls at the insurance help desk and the AI kind of response on what queries the person has called in. And of course, like in all demos and POCs do, it worked beautifully. And the second question was, hey, let’s scale it up. And immediately the person with whom we were working, the CTO said, you know what, let’s do it in English for phase one. And let’s look at other languages later.

Now, my argument was that if you do it this way, most of the folks who are calling you are the ones who speak Hindi because you are operating in that region. If you don’t do that, you are alienating 70 % of the people and your customers. And why are you then putting this bot for? Why are you even attempting it, right? And their answer to that was, you know what, I have to show ROI from AI. And I have to show that quickly. And hence, hence, please go and still do the English one first. Let’s look at Hindi in Phase 2. Right? And you can imagine what kind of heated conversations I was trying to explain them. That’s not the right approach.

You need to be thinking of Hindi right from the start. Because if you do this, it will work beautifully in demo because it was all English. It was a sample data set with which you were working. It may still work in your Phase 1 a little bit. but in phase 2 it’s going to fail miserably and it will bite you even bad when it comes and fails at that point in time but it was a hard conversation we finally convinced them but to get to that there was a lot of education that’s needed so what I’m saying is if you look at these 3 examples where in certain cases due to the virtue of the business that humanitarian agency was you had to be accessible by design in the second case because it made good business sense the company said make it accessible whatever financial solutions we have whereas in the third case it was a very difficult conversation on accessibility because somebody wanted to prove a point to their management that AI gives the ROI which is there now if I look at various cases where most of the corporates are today of the businesses which actually are dealing with this economy and responsible for bringing AI out there, most of them are still hovering in the bucket three, which is the last one, where it is still not inclusive by design, still they feel, I have a POC, I can scale it up without being as inclusive with the data, with languages, with other things, and I can do that in later phases.

So this was the story till the entire last year. This year, and thanks to the summit and more and more forums like these, businesses are appreciating the fact that if they don’t do inclusive by design, they are leaving money on the table, and it’s just plain, smart, good business. So I think now the conversations in the boardrooms and the rooms and in corporates are shifting, where the question is not necessarily, get me the ROI and prove and show that AI works. but make AI sustainable and working for me for a long term, which means I have to be inclusive. So that’s what I would say.

That’s wonderful. I think, I mean, kudos to the summit. It certainly made the conversation inclusive, really common and very boardroom, entered into the boardroom finally. So I think that’s a good takeaway. I think moving from the third bucket to now, Agastya, I wanted to come to you to just help us understand the way we, what we’ve seen in the research findings of our report has been that in many ways, AI is a force multiplier. It is going to enable at a much faster, at a much larger scale, right? So tell us a little more about the, at the back end of the design team in Meta, how when you look at designing a particular device, what are the instructions you give your team that this is what you need to follow A, B, C, D, so that the device you’re creating is definitely inclusive.

It respects the idea of the people that it’s going to be useful for.

which are the divots on sidewalks that allow wheeled devices to transition from a sidewalk to a street to cross the street, they are ubiquitous in the United States due to regulatory pressure to protect the rights of people with disabilities that use wheelchairs. But anyone who’s encountered them while using a pram or stroller or a trolley, shopping cart, or luggage has benefited. They just make cities better. And so taking an extra step and thinking holistically rather than just being pressured by regulation, which of course is still an important component, is critical to making the end result good. I don’t think anyone’s perfect, but I’m doing my best to instill this mindset within Meta.

All right. I mean, yes, I don’t think anyone’s perfect, and we’re all trying our best. It’s a good takeaway from the summit and everything that we’ve learned from here. I wanted to pivot to the conversation around investments and just, you know, how do you make inclusion and creating sustainable pathways? For inclusive AI, really, you know, in the context of India. or even globally for that matter. And I first wanted to actually come to Olivier again. Could you help us, just give us some idea of how did you go about making the procurement policy, which I understand is very innovation -friendly, for the national AI strategy? What were the considerations that went behind it, and how have you seen it pan out on ground so far?

That’s a good question. So, Remy, paint a picture a little bit. So you see the whole journey to get to there. So procurement is normally most seen in public sector. And, you know, we are in a country where accountability is something expected from everyone. And when it comes to public funds, it’s even to another level. which leads that the classic procurement, if I can say, it takes a lot of time because in order to really avoid any way of any conflict of interest in the process but when it comes to the ICT space most innovation products look at the journey, he’s talking about about graphic user interface and you know the touch screens, look at the social media, he’s from meta you know, Facebook before before it become meta but just if you look at the journey you will see that normally into this space there is a change, there is a new thing every three years 2023 we were talking more about DPI, DPG and people were even having hard time to differentiate the two And now, three years later, we are talking more about AI as if it’s a new thing, but it’s basically the large language models that are new because of the revolution of social media that gets a lot of data sets and creates something that we can interact with.

If you go into the all -time procurement, you can try to buy 10 phones, and it takes you three years, which means basically by the time you follow this process, things have changed. You may have the right process, but not the right product because things have changed. That’s how the idea of now having the public procurement for innovation concept. Which was put in there, let’s say, in some space to some categories. Let’s consider a way where instead of really going through the classical time, how about… we bring together key players, potential institutions that can deliver to XYZ solution that we see is needed. And then give them a chance. That is a bit like, you know, they compete to see the best, who can deliver to this, and then they are empowered to do this.

So we go more into the agile mode of having these, you know, small step development along the way that can adapt to the change instead of waiting for that long process and end up getting a product that is no longer relevant to the market or to what we need to respond. Or maybe it’s relevant, but it’s way too old. So imagine trying to get, now we are at iPhone, what, 17? You know, how many times have we seen these basically these evolutions? So think about the process that started five years ago. It works for building roads. but not necessarily for technology projects. So that’s a bit of the picture of how we end up to this.

That’s interesting. Even for us in India, it’s been that oftentimes the law and the policies is playing catch up with the tech. So you really need to find a creative way of finding solutions that you can smartly look at regulating the emerging tech. Aragi, I know you have a lot of thoughts on this one, especially around the procurement rules and how do courts adopt your product. Please do come in. We’d love to hear more about how do you think the existing procurement rules have shaped the way you’ve been able to access the courts and deploy your product there? And what do you think needs to change so that it’s faster and more usable for the courts?

Yeah, I think I’ll take a more solution -oriented. We could talk a lot about the problems of policy playing catch up with tech, but I’ll take a rather solution -oriented approach to how we… We’ve worked with the courts at Adalati. So when we started Adalat AI, which is about two years back, AI was very new. Courts are still, you know, working to adopt generic software technology. And so AI is extremely new, right? I think a couple of things worked really well for us. Number one was to build painkillers before vitamins when it comes to solutions. So we actually went for a very big pain in courts, which is judges are having to write everything down by hand.

And so when we say that, hey, there is this new technology, but it solves a really big pain point of yours. This is not a vitamin. This is something that you are all struggling with. There are a lot more open to adopting technology. But in terms of the creative solution around procurement, I think I want to emphasize that nonprofits as a pathway to creativity, creating impact are highly underrated. specifically in the space of justice and law. You know, there are all these non -profits that work with education and with healthcare to support doctors and teachers, but not enough non -profits doing this to support our court staff and justices in the country. And so, Adalat AI is exactly that.

Now, what do I mean by non -profit as a vehicle? Being non -profit helped us align incentive with the courts better. It automatically took away a lot of the stress around, oh, what are they going to do with my data? Are they going to profile the judges? It took away a lot of stress around, okay, are they going to charge me? Where am I going to, how am I going to evaluate the new technology? Now, so this helped us get into courts initially. And, you know, within two years, we are now in nine Indian states. We are in one out of every five courts in the country. And as of a historic mandate by Kerala, it actually became, mandatory to use Adalat AI in every courtroom in the state to record witness depositions.

It’s absolutely not allowed to do this by hand. And I do think that, you know, sort of this impact vessel vehicle really help us do that. In terms of sort of the other side, which is that at the end of the day, eventually courts and all institutions need RFPs. They need to sanction budgets and they need to sort of make sure that they pick the right player for it. Being a non -profit, some of the ways in which we are seeing we are able to influence this process is that now that they’ve been able to work with us, they have a lot more experience of what it means to scale these products. Their tech teams have a lot more experience of working with us in knowing what do they actually need out of these products.

And so they have a lot more in terms of ideas of how to draft the RFPs. And so I think that’s the other big benefits. If it’s that coming from being a non -profit, you know, all these non -profits in the ecosystem, they’re able to help these institutions. sort of design better RFPs when they actually do go and procure solutions.

Right. That’s interesting. I love the Kerala example. I wish to see that happening across all states sooner in the country. But I wanted to now move to Agastya. I know that Meta Ray -Ban Glasses represent a significant investment for Meta in terms of the AI -powered hardware that you’ve created, right? From the inside, help us understand how do investment priorities shape the design journey for that product?

That’s a good question. And I think in reality, sometimes the plan or the intent doesn’t necessarily match with where things land. For example, the Ray -Ban Stories, which were the first iteration of smart glasses we shipped, they were great. They had some really cool features. When we built them, we initially thought that the use cases would be around taking pictures and audio would just be used for making phone calls. While myself and a couple other engineers were doing hackathon projects, combining multimodal AI to help blind and low vision people, this was before the AI hype had caught the zeitgeist of the industry. And then the next iteration, the big focus we put was we found that people were using them for music much more than we expected.

And so we thought the biggest use case, the biggest investment would be on making the speakers better for Ray -Ban Meta version 1. And we did that, and the music and audio quality was much better. But you’ll notice something missing from the product plan that I mentioned for both of those products, AI, which is now not only front and center, but it’s literally how we market these glasses. They’re AI glasses. I say this not to drive cynicism, but nobody has a crystal ball. And so I think the key thing is learning to be nimble and understand the direction things are going and being able to jump on trends versus being too fixated on what the original plan was and maybe giving a sunk cost fallacy.

I love the painkiller. Vitamin analogy. And maybe adding to that. the really important thing to do is to avoid the temptation of eating the candy before either of those two. That’s my take on it.

That’s interesting. Thanks so much. Arshna, I wanted to come to you. Same question, and I think you touched upon it in your earlier remarks that the executive wanted to show ROI. So really the question is when you have these routine discussions in boardrooms with your enterprise clients, did you start with positioning inclusion as a CSR initiative or just a good -to -have thing in your strategy, or has that shift significantly changed? I mean, of course, barring the summit and the two months of change in thinking, but in the past, how did the pitch start for you, and how was the reaction really like from the leaders?

If you, and this is my personal view based on what I’ve seen, and my experience, If you position inclusion as a CSR initiative, you are also going to get budgets which match the CSR initiatives, which don’t necessarily translate to good products or make good economic sense. So that never works. Don’t do it. That’s first. The second is that when you are positioning these kind of conversations, remember that in corporate world or any business for that matter, it’s always a trade off. How much you are willing to spend versus the returns that you are getting. Now, if you want to be it more and more inclusive, especially in an AI context, you can do that. If you have more and more diverse data sets feeding into that.

Do those exist today? at a cost which is palatable to all enterprises? The answer is no. So first thing what enterprises look at is great, I want to be inclusive, nobody wants to say no. But if they don’t have those data sets or if the cost of getting those data sets or cost of cleaning those data sets to make it more inclusive is going to be much higher. So typically in AI we say $1 spent on AI, you have to spend $3 on data. So if that’s the kind of economics you are dealing with, there is definitely going to be a point where the company says inclusion is going to come later because economically it stops becoming as viable to them.

Now if you look at the inflection point that AI is there today, it’s hyped up. it’s yet to show tangible outcomes across all the sectors. Yes, it’s shown great promises and results in some, but has it universally shown those promises? No, we are yet to see those. So when you are dealing with clients which are in those areas where they are yet to see those, you will see inclusion taking a backseat, not because of the intent, but because of the cost in certain cases. Whereas everybody realizes that inclusion is plain good business, but those trade -offs is what they look at. Increasingly, with the data being made more accessible, governments taking initiative. In fact, at India, we have AI Kosh, which the government of India has put in where you get diverse data sets of India and you can feed in those data.

And you can use those data sets to make your AI systems more inclusive. more tuned to custom local traditions, you will see the cost of this implementation going to come down. So economies of scale kicks in. Moment that happens, you will automatically see corporates and companies adopting this because now while there always was intent, now that intent is also becoming financially viable for them. So I would say it’s a combination of these kind of different facets which play together when certain decisions get made.

That’s helpful to know that CSR is not the go -to route to see, but a bunch of things that determine the decision -making. I think in the interest of time, I’m going to move to the last segment of our panel discussion and my favorite, which is design. So I think I’m going to first come to Agastya again. Tell us about how can AI devices really drive accessibility first innovation? And I remember reading this at the Metastore, as well, earlier in the… weeks. So just help us understand the company thoughts behind it and how have you gone about executing it across different devices, including the glasses?

Sure, thank you. Accessible design is good design. Universal design is good design. I think opening with that mindset that if you build things in an inclusive way, you make the product better for everyone, people with and without disabilities. I think that’s the critical factor. I think the second thing tied into that is the notion of nothing about us without us. On this panel, we discussed that a model is only as good as its data set. The same is true for a development team, for an organization. So I think it’s critical to hire people from all sorts of different backgrounds, not be stuck in your own bubble because you’re building products for people with all sorts of backgrounds.

It’s not just good karma. It’s not just charity. It’s good business. So I think those are kind of the two philosophies I’d push on, is that hammer home that innovation actually is seeded by accessibility. There’s so many innovations that started from accessibility efforts. The flatbed scanner, text -to -speech synthesis, OCR, these started as efforts to read books for blind people. They didn’t start as just industry -wide things. And yet here we are. So I think working with your leadership teams to call those examples out, show concrete examples of how things get better, and ensure that you are building with everyone.

That’s incredible. Thanks, thanks. I know in the interest of time, I’m just going to quickly come to all three of our panelists to help me understand, of course, obviously with your own case study, but, Olivia, one case study from your country that you think the design aspect of it where, you know, from the very initial you’ve looked at, and inclusion has just been visible, and that’s helped in many ways. So just give us one example. and the same thing for you, maybe perhaps from the jury that you looked at on AI for her, that would be helpful. So, Olivier, then again, then Archana.

All right. A quick one. We don’t have so many AI -powered solutions that are there, but just an example, we are working on an AI -powered advisory solution for agriculture. And right from the beginning, we need to think about the end user before we even think about the technology, because what AI is doing to us actually makes the tech easy. You know, a chat bot and a robot, it’s like even a code bot can make the code. But the end user now, in this case, we are talking about a smallholder farmer who does not use a software. He doesn’t use a smartphone, but uses a future phone, who may be in a place where the connectivity is shaky.

but who only speak Kinyarwanda. So going from that angle now basically there is that inclusivity right in the stage so that if we can deliver to this then the technology can work. That’s one example I can set in there and a couple months from now I should tell more success story because we are beginning into now those solutions to scare.

That’s good. I look forward to a couple of more months and then some more case studies from your country. Raghav, do you want to go next?

Yeah, I think I’ll talk about two things. Number one is design and design of product and the second is and I want to contrast this is design of the intervention itself the entire solution and with respect to the problem that you’re trying to solve at Adalat AI with respect to design of the product there’s one thing that we’ve done from the start that has helped us we force our engineers, designers, everyone to go to court sit with judges, show them the designs, get an in -person approval from them before any piece of code is written, before they come back and touch their laptops, right? And that’s one thing that has helped us tremendously in being able to make sure that design is extremely inclusive.

The second is when it comes to design of intervention itself, it’s not enough to build technology. You know, we build transcription solutions, but if the judge doesn’t understand that they need to turn on the mic at the podium when they’re kind of dictating, then the mic just becomes a very expensive paperweight, right? It’s of no good use. And so we do extensive trainings. In fact, we have something called the Adalat AI Academy. As part of that, we go to courts, we teach them how to use the technology, and we had a very interesting insight. We were trying to teach them AI. But what we learned was a lot of judges don’t know how to update their Chrome browser.

And so that helped us then understand what exactly is needed to drive that intervention forward and make sure that impact is actually realized on ground. And I think now a lot of Adalat AI Academy has become a part of the official curriculum of becoming a judge in a lot of states. And so that’s kind of helped a lot in terms of design.

That’s great. I think moving into the curriculum always helps that you’re planting the seeds early on for the training. Archana, the last word.

I’ll be real quick. So as part of the jury for AI by Her came across several startups, which were, of course, led by women and conceptualized and supported with AI. One of the startups which kind of stuck with me is a startup in fashion tech. And the interesting piece was that that startup. Helps the designers to show and envision how the finished product could look like. and what it does is not just show it so that you can reduce the time it would take to develop certain samples and then discard them so it’s not just sustainable fashion and sustainable designing, but it also shows in different shapes and sizes. So that makes it even more better and inclusive.

So some of these kind of things is what I found in the solutions which were there in AI by Her, which kind of makes you think that, yes, these are truly being sustainable and inclusive by design.

That’s wonderful. All right, do we have time for questions? No? All right, cool. So we’re going to… Sorry about that, audience. Thank you so much. But probably we can catch all of the panelists once you’re done with the last segment. Thank you so much. Thank you. Thank you so much for a very insightful panel. I think everyone who stayed back has at least the last hour has been more informative as well and we were left with something. from all of you. So thank you so much. Please do catch the panelist. Thank you everyone for staying here. I know it’s been a long week. This is the last session at the AI Impact Summit, so just thank you all for being here.

And a big shout out to Metta who’s partnered with us for this project, so thank you for your continued support and we look forward to engaging further work. Thank you all. We do have some mementos from the India AI Summit for all the participants. So Rutuja, if you would please give them Yes, there is. Thank you.

Sharing is learning with the rest of the world. One, an AI that is independent. From large global AI to empowered, scalable, sovereign AI. Sovereignty. The generation sitting right in front of me grew up taking it for only political and geographical individuality. Fast forward to now, the world has a completely new landscape for its definition. I’m growing up knowing it’s to be more around something I may like to call digital independence. And achieving AI sovereignty has become a global imperative. And then I’m seeing an emergence of very AI models which are not just differentiating from the rest of the world. by scale, computer parameters, but by the very approach different nations are building them with. While US leads the global AI models and the technology sector drives innovation, China likes to keep its control centralized with rapid scaling and strong international governance.

While Europe likes to build it more with trust and compliance with the world’s first comprehensive AI law, Middle East positions itself by building AI hubs in the infrastructure layer contributing critical nodes in the AI boom. Well, India is digging into sovereignty. Data sovereignty, infrastructure sovereignty. And most importantly, talent sovereignty. And I’m glad. That is what my country needs to boost its economy. Two, an AI that is inclusive. From the artificial general intelligence race to responsible, democratized AI inclusion. The democratization of AI with inclusion, which I touched upon in my keynote at the EIFGO Global Summit in Geneva last year, has become a core focus area for not just India, but even for the United Nations and the rest of the world.

I’m seeing how India is leading a shift from the artificial general intelligence race to the AI. Two, responsible, democratized AI inclusion. The democratization of the AI course as a key enabler for India’s digital public infrastructure 7500 data sets and 273 models have already been deployed as natural resources to build AI solutions across sectors. Allow me to share my two cents on the affordability of AI compute power under the India AI Mission. Well, to your surprise, it is less than 2 cents per minute. How’s that for democratization? Inclusion of different Indian startups, researchers and developers. Social inclusion of different cultures, languages, disabilities and even gender equality. Overall inclusion of human capital, innovation, social empowerment and the list goes on.

Third, AI is free and AI that is impactful. From safe, innovative, actionable AI to impactful AI. Let’s move to impact and let’s do it a bit differently here. How about I share my own use case of an AI model just released by India. Thanks to my recently completed certification course from the India AI Mission, I observed how every single bit of content was exemplified with an India specific use case impacting lives, businesses and industries. So here’s my back story. When I was six, I written a book on AI. Are you born with AI? This had been made available globally on Amazon and even had been acknowledged by His Excellency, Secretary General of the United Nations, Sir Antonio Guterres and the Ministry of Education, Government of India.

Thanks to the full stack AI sovereign model now in place, Sarvam AI, I’m able to translate my book into 22 different Indian languages, boosting the sales of my book and contributing to India’s GDP. Here’s a sneak peek into this. So you can see here that I’ve translated it into Punjabi, Tamil, Hindi, and then 19 more languages, but obviously I can’t fit on the slide. Impact? One, it helps me live my dream to drive A -L -O -C to all my friends out there breaking language barriers. Two, it helps me support the National Education Policy 2020 of the Government of India by introducing A -L -O -C from Grade 3 onwards. Democritization checked. Three, it helps to have a wider reach as an author, boosting the sales and the royalty I get from the book.

Business impact and GDP contribution checked. So, if a Gen Alpha can contribute to AI literacy countrywide by first writing a book on artificial intelligence, then using AI tools to make illustrations to make it relevant for young minds, and then further use Indian AI tools to translate it into multiple Indian languages, boosting the sales of his book and the royalty, then, to contribute to India’s GDP at age 8, I am confident that each and every one of you can leave your impact with relevant Indian AI models. Amalgamating, be you geopolitically driven or an inclusive AI -impact fabric, and there is no assurance that any country will get it all correct and do it truly. My simple yet important message here is that we can all learn from each other and share our learnings to make this world more empowered with AI.

And that is exactly what India is all set to do once the GP AI Council members convene and define the multilateral cooperation for responsible and inclusive AI, keeping in mind the value of a human connection. Also, me and my generation are part of this AI revolution too. We understand and observe how AI is being shaped up globally. Be it governments, be it tech giants, be it start -ups or even scientists. We are not just at the receiving end. Do not forget we are born with AI around us and we will contribute and be the true agents of change of what you all build today. I stand for I, Generation Alpha. I stand for India. I stand for impact.

And the world will witness all three when they have been raised to the power of AI. Sarvajan Hitai, Sarvajan Sukhai. Thank you.

Thank you. Thank you. Thank you, Renvi. We have our next panel, which is next generation of techies. May I now invite Mr. Arvind Jain, Founder and Chief Executive Officer, Glean. Ms. Navina Singh, Founder and Chief Executive Officer, Credo AI. Malhar Abide, Chief Technology Officer, Origin Bio. And the panel will be moderated by Mr. Ranirudh Suri, Managing Partner, India Internet Fund. In the meantime…

And it’s such a pleasure to be here with such lovely panelists and an audience who’s possibly going to skip some of the lunchtime to join us today in our discussions. Let me get started by really talking about, you know, we are towards the end of the week. It’s been a fantastic week, lots of conversations. And one thing which I reflect back on most of the conversations has been what is the most defining question of our time, which is who all is artificial intelligence really benefiting and with what rules? If I look at it, AI’s enterprise infrastructure, AI’s public sector capability, AI’s even geopolitical leverage is what we’ve seen across all these days. But more importantly, AI has become a part and parcel of our daily lives.

It stretches from everything from making our work life easier. to making sure that we get our entertainment as and when and how we require it. And more importantly, from healthcare to hiring to anything you can possibly imagine. When we really focus on inclusion in AI, one thing which has kind of stayed as a thought for the last five days is inclusion in AI is way beyond equitable representation in data sets. It’s, you know, it’s everything. It’s about access to compute. It’s about standards. It’s about having a right policy framework, which encourages everyone, everywhere. And more important, it’s also getting clarity on regulations, which are there across countries, to see how it can really be beneficial.

Now, to take the discussion ahead, today’s conversation is going to be really about trade -offs. Excellence and inclusion. It’s been interesting on how to navigate both these terminologies whenever you think of any policy or a framework. So I’m going to start with my first question to Dean. So Dean, you know, you’ve been working at the frontier of AI policy. You’ve been at the institutional design through the Foundation for American Innovation. There is a lot of growing debate between self -regulation and innovation -first approaches. Where should policymakers really draw the line without really undermining national competitiveness?

So I think it’s a, first of all, thanks for being here. And thank you for having me. It’s an honor to be here. The way I think about this is that, you know, we will govern AI through a very large intersecting web of different things, right? It’s not just going to be one day one bill is going to get passed and that’s going to be the AI bill and then AI is regulated, right? AI is currently regulated today. It’s regulated by many different things. It’s regulated in the United States by things like liability doctrine and a lot of existing products. regulations and things like that. So I think step number one for government is let’s take the existing bodies of law, you know, many of which just as, you know, as in India and the United States, we’re quite proud of.

Many countries, you know, are very proud of their regulatory and legal traditions. We have a common law tradition in the United States that we are proud of. So let’s take those things and let’s figure out how to apply them to AI. And then, you know, the companies, I think, thus far, the major AI labs have been, I think, responsible stewards when it comes to the major risks. Now, I think the area where you might need proactive governance first is, at least in my view, is really this domain of tail events, potential events that could be very serious, have very serious consequences that are relatively unlikely. So, So, you know, pandemic is an example of a tail event.

And I think AI might have some tail, you know, sort of catastrophic type risks associated with it. And so this is an area where some proactive governance, I think, is needed. And I’ve written supportively about transparency laws in the United States along those lines. So I think that’s where when we have a clear and demonstrated threat model and we have a, you know, clear evidence that existing law is not sufficient. I think one area, one aspect of AI governance that I often push back on and that I often dispute is there’s this kind of assumption baked in whenever we talk about AI regulation that the existing law is insufficient and that the current status quo is that AI is unregulated in some way.

And I think that should actually be, we should have the opposite presumption. We should presume that existing law is sufficient and that there is some sort of good solution. And then. Yeah. It should be, the burden of proof should be on the person who wants the regulation to show this is why existing law doesn’t work.

Thank you, Dean. That’s very interesting that we go with an assumption. And with that, Gabriela, let me move on to you. So should, you know, how can governments foster open innovation, assuming to whatever Dean said, while minimizing the risk of market distortions?

Well, I think that it’s a very nice segue because I completely agree with Dean that there are a very broad portfolio of policy interventions that has not only to be with regulations. Regulations is looking at the way the technology is developed. But we need to think about this as an ecosystem that needs to be nurtured, that need investments. That needs incentives. that needs institutions and that needs infrastructure. And therefore it’s not only the technological conversation about what do we do with AI, but what kind of an economy we want that is really productive, that delivers for people with AI, and for that you need government intervention. And let me tell you, what is very interesting is we usually tend to think that the private sector is an innovative force and the government is a break.

In the U .S. that was not the case. The U .S. was the place where the massive investment in innovation in DARPA, in the creation of the Internet, all the foundational issues that we are seeing now were financed at some point by basic research that was paid by the government of the U .S. And many countries fill that space and that’s why it’s so important that we invest in research because it cannot be that the research is being done only by the private sector. and then it’s also true that when the government gets into the research it’s open research because it needs to bring everybody around the table and then it needs to be shared which is not always the case when you have a private sector innovation so I will contest this also way of framing the issues in terms of the government’s only creating market distortions because at the end it’s about how the government can be effective to address the market distortions that we see many times emerged in this case I like to see the AI technologies as natural monopolies somebody invented something somebody laid the whole network to operate it and then it was a monopoly and now it’s oligopoly at the end it’s very concentrated so there are market distortions now that needs to be addressed by government policies again there is a wide gap of things that needs to be done to ensure that the main distortion that can occur nationally and globally, which is that this is a story of a lucky few, is prevented.

Thanks, Gabriela. And you know, it’s interesting you mention that because at least in India, whenever we speak about public -private partnerships, it’s all about how we are moving from a culture of competition to cooperation, to really working together so that the markets stay healthy. With that, we move over to you, Amanda. So, Amanda, you know… Eva. Sorry. Yeah, Amanda is missed. So, Eva, let’s talk about the global AI governance strategy at Wipro, right? Many organizations are developing a responsible AI framework. How do we move beyond policy statements? Through measurable accountability. and specifically when we have to do that at scale.

Thank you very much and it’s great to be here and thanks to all of you for joining. So I have what I say often, I have the best job in the world, which is basically to translate a lot of the things that we’ve been discussed over the last few days into practice. So basically means we’ve heard democratisation, we’ve heard inclusion, we’ve heard how it’s important that AI is inclusive and by inclusivity, it’s not just about access, as was said, but it’s also making sure that many get the opportunity to participate in the design of this technology, but also in the decisions around what we are producing and who is going to be benefiting from that.

We, I think in a lot of our work, a lot of organisations, what happened over the last few years when generative AI came about, a lot of organizations we had to face something quite dramatic if you think about it because before then AI was very much for engineers for scientists to work with if they think about machine learning people who knew about AI then what happened a few years ago is that generative AI came and everybody got access to it did you remember and do you remember how companies started to scramble with who’s got access do we leave people our employees to access this the systems do we create our own private instance how do we navigate the fact that we want people to play with these tools with the fact that we have to be safe and secure as an enterprise and then things evolved and a lot of organizations if you know how the debate around governors started and you know a lot of organizations started to set up governance boards and they started to set up ethics boards and all of it and I think we realized at some point and I took on the challenge of AI governance from a privacy standpoint the reason for this and many people in organizations took on AI governance from a privacy standpoint not only because a lot of AI harms are actually privacy harms but also because privacy professionals knew about risk management and then we realized that actually governance of AI is much more than that it’s much more than risk management it’s much more than compliance we realized and I think this summit show that really clearly that AI governance is really about a strategic capability that an organization must have to create long -term value What does that mean?

It means that you have to do two things. First, you have to look at what you want to deploy or develop and that is where you need to embed privacy, security, legal protections, resilience into the products that you’re working on. That is not an easy one. It’s not an easy one. It requires knowledge. It requires investment in privacy enhancing and security enhancing technologies. It requires what, for example, India is promoting which is a techno -legal approach. It’s not just about the law but it’s also about how you translate the law into technical tools. So you have to do all of that and then you have to look at what happens once the product is in production.

So how do you monitor it once it’s out in production? How do you make sure that if, for example, you’re using AI to fire and fire, as sometimes it happens, you have tools to pull the trigger if something goes wrong? Now we are into the realm of agentic AI. If you’re interested in this, I’ve just published an article on the World Economic Forum of a subject I’m really fascinated in, which is what is the design for trust in agentic AI? So, for example, governance means that you do design these agents, but you give people, according to security standards, but also according to their own preferences, the right to intervene when they don’t want the machine to make a decision in an autonomous way.

And then you make sure that you protect from cascading hallucinations, from model drifting, all of that. So governance, to me, is very much about… . the capability that organizations have to think laterally about AI, which means impact, design choices, the trust stack that enables people and employees to trust the product. And one element which to me is very important is to make sure that companies bring their employees with them. That is a very crucial part of governance because the work is going to change. People are going to change the way that they work. They’re going to, and it’s important, the people are going to know best how to use AI are the people working in a company.

This is why I’ve seen successful companies developing a lot of use cases based on their activity and asking their employees, how should we innovate this? This is a fundamental part of governance, I believe, because it brings people with them. us. So very encompassing approach to governance. I think we are evolving and changing how we see it but certainly I think it’s become very clear over the last sort of few years and especially with things like this summit talking about impact that it’s way beyond compliance and it’s way

Thanks Eva and that makes me very curious enough to ask you a very quick question. So do you still feel you’re underestimating the risks because you spoke about AI trust?

No, I think let’s be honest here. Over the last few years we’ve seen amazing benefits coming from AI, right? Beautiful stuff, fantastic. Every day there is a piece of news that makes us hopeful that we can improve our well -being and we can feel better in the world we live in. But at the same time we’ve seen the risks too and we’ve got to be honest that looking at the success without looking at the risks is very naive. We can’t. because we’re not going to be able to deploy AI successfully if we don’t look at the risks. We’ve seen disinformation. We see deepfakes. We have seen AI softwareizing existing inequalities into decision -making around people, future, rights, and livelihood.

That’s not okay. So we’re not underestimating the risks. But we can’t approach governance from a risk management control. We have to shift our approach and do AI for good and change the way that we look into this. So we have to engineer fairness into the systems that we create. We have to engineer inclusivity into the systems that we create. And, of course, we have to manage the risk. But the mindset has to really shift.

Thanks. And that gets me back to Dean. So my question to you is, inclusion at the national level often intersects with computer access. And research infrastructure. You spoke about public -private partnerships, spoke about trust in… emerging technologies like artificial intelligence and maybe even quantum going ahead, should governments treat compute as critical infrastructure?

Yeah, I think they should. The data centers that power frontier AI systems are going to be a part, you know, like ports or railroads. They’re going to be critical infrastructure of the future. I believe that’s true. Prior to my current role, I worked in the Trump administration in the White House Office of Science and Technology Policy. And in particular, I was one of the people that shaped the administration’s AI action plan and AI export program, which my former boss, Michael Kratios, was just here talking about and announcing some next steps on. I was really excited to see that. One of the key messages of that that I feel was I feel this is maybe a communications failure on our part.

But. You know, the United States government has publicly said the president. has come out and made as a flagship of his AI policy that we intend to subsidize the development of AI data centers in the global south. That is a policy of the United States under this administration. And we don’t have the interest in exercising control over the technology in the way that I think the prior administration did in some ways. We don’t want to control other countries’ use in the same way that the prior administration did. So I do think you should think of it as critical infrastructure. And I think that you should think of the United States as a partner in the construction of that.

And I think that owning infrastructure of this kind is an asset that states and regions can use for years to come.

Thank you. So… So, you know, it’s been interesting because whenever we speak about AI at scale, when we talk about taking AI to every single person… across the planet, there are always three vectors we look at. So that can be mindset, skill sets, and tool sets. You just spoke about tool sets, which is extremely relevant. And that takes my question to you, Gabriela. When we talk about mindset, should inclusion be framed primarily as an ethical imperative or a competitive strategy or even both? What’s your take on that?

I really like the way this question is framed, my dear, because I’m sure that people think that going for inclusive policies might hinder competitiveness. Who from the public believes that? Can we have a show of hands? That being inclusive might hinder competitiveness? Investing in competitiveness might go against inclusiveness? I really think I’m an economist, and I think in this area we really need to think. We need to think about economic inclusiveness. because if we just think about social policies that might be needed when some people are left behind and therefore we need to invest in communities or in infrastructure or in people, kids that are in deep need of education, those things are very important.

But more importantly, we need to consider how do we foster market economies that are inclusive and that’s the core issue here. And I can tell you because I have been looking at the question of inequalities. Actually, I’m now co -chairing the task force on inequalities financial disclosure. And what we have seen is that when you have market concentration, productivity flattens. And what happened here, and we saw it at the OECD report that we did some years ago, when you have concentration at the top as the one we are seeing now, we saw that the OECD report which is companies having the whole concentration of compute capacities, the capacity to sort out skills and attract the skills, the capacity of having the financial means to invest.

What happens is that the diffusion machine, which is this very important element that trickles down the innovative developments into a broader set of users and benefits, is broken. Now the diffusion machine is broken. And therefore we need to see how do we ensure that the diffusion is faster. And to do that, of course, I agree with Ivana. The question is how do we ensure that we create the capacities of people and economies that are lagging behind. But we also need to see how do we diminish market dominance. And I know that there are many other considerations, geopolitics, competition matters, trade secrets matter, all these things matter. But for me, competitiveness, inclusiveness has to do with creating the highest well -being for people and that’s the outcome and that’s where ethics competitiveness, all of these narratives collide together because at the end what are we looking at?

that we have well distributed 70 % of wealth in many countries, 60 % of wealth 50 % of wealth is owned by just the top 10 % income groups but that’s not sustainable I get into Europe and Mexico and I was asking where do I put my children because I need good schools and they told me choose the right neighborhood that’s not possible and therefore I feel that there needs to be this set of policies and who is there to ensure a level playing field who is the one that needs to be using the tax systems or the incentive systems or the investment systems or to ensure that people are not left behind or the anti -competition or the non -competitive practices who is there to I pay my taxes so that the governments deliver on their promises so I think this is super important and I feel for example that what India has managed, this question of the digital registry I was with Mr.

Murthy when he presented his plan so many years ago I never could believe that you were going to be doing registry for 100 million people every month, it was just like you’re crazy, that will never happen who finances the government and now you have all India with the digital identification, it’s just amazing and then you go with the financial thing so I feel this is this is this is the this is the this is the this is the this is the this is the this is the this is the this is the this is the this is the this is the this

Thanks, Gabriela. Since with this vision that the world of tomorrow with AI would certainly be a better world and hopefully be a better world than what it is today, I have a common question for all the panelists. And the question is, what do you see as the most significant blind spot in recent times AI discourse, keeping in all the conversations you’ve possibly had this week and even prior to that? And maybe what we can do is, Dean, we can go with you first.

Yeah. So in terms of blind spots, I think maybe the most important thing I could possibly say here would be one thing I’ve heard repeated a lot in the conversations I’ve had this week is this notion that the frontier models, the best AI systems are not… necessary. You can find good enough models that can, you know, that are cheaper to run. And in some cases, I think that will be true. But I would point to the very significant blind spot there that, you know, I believe that what we are doing is building systems that are going to be smarter than humans at all cognitive labor. That is a very serious goal. The United States is currently spending, like, it’s not a joke, right?

That’s not a joke. That’s not hype. That’s not crypto. We’re spending a trillion dollars this year on that. That’s the plan. We’re going to do it. It’s going to happen, right? And so the capabilities of those systems and the way that that will change the way the world works, I think ambitious people will be able to do unbelievably broad range of things. And I think this could really be an incredible opportunity for countries in the global south and really everyone in the world to participate in building the future together. And this is not something rejecting frontier models out of some sort of belief that that preserves sovereignty because there are existing use cases you can think of that can be done you know with with with cheaper models i would use a think of think of frontier ai as being useful for stuff that uh we don’t even have words for today right concepts that you will invent uh and you know that we will all invent together that’s the future that we’re building um and uh i think that’s a it’s an easy thing to miss and i think missing it is basically missing the ball game

that’s very interesting thank you for sharing that gorilla what about you

i would say a traditional education education education okay i am really that takes awake my sleep why aren’t we upgrading massively the education pedagogy Why are we changing the way we go in school? Why don’t we invest in our teachers for them to understand how these technologies can help improve student outcomes and at the same time make their life easier? I see a lot of teachers complaining about all the administrative work that they need to do that don’t leave any space for them to invest in quality changes with their students. And I’m not seeing that happening. And we need that pipeline. If the future that Dean is projecting is going to happen, is going to arrive, we need people to be very well equipped.

And where do we get that equipment? I’m fine to invest in the workers in the market. That’s very important. And I think that we need to upgrade that too, the skills of the people in the market. But the school system needs to be upgraded. And actually, I haven’t seen it really happening anywhere. This is a challenge. This is a challenge for North, South, East, West. and I invite that for all to confront this challenge.

Okay, I love the fact that you brought education and skilling as a part of it because building AI readiness has become so essential to ensure natural competitiveness no matter which market we are talking about. And just to share an incidence, India was one of the few countries where AI was introduced as a school subject way back in 2019, even before the COVID era. So students could learn AI as they would possibly learn a biology or a physics. But yes, that’s a major challenge which we’re trying to work on. Ivana, your book.

I was very impressed yesterday when your prime minister spoke. And I was very impressed by one thing that he said. But he said, you know, develop here and serve humanity. And I think that to me went to point that it’s been very strong. here and where I see because the I like also something that has been missing so far so he said something very very important and he said that AI needs to be used for inclusion for economic well -being inclusion as we said as access but also as participation for many as reduction of the gap between areas of society in geographies across India inclusion also as creating models that respect your languages and your dialects and the ethical norms bind in this country together because the eye that we have now is often not reflecting of the diversity of the world one thing that following on this one thing that has been good has been to see many leaders coming from all over the world.

One thing that I’ve always supported is how we haven’t aligned, I’ve always thought this, how we haven’t aligned on what the red lines of AI are. Are there things that us as a society or as a world, we are never going to do or we don’t want to do? Regardless of, are they, and we’ve seen appeals coming over recent years. We’ve had massive debates around ethics of AI in different ways, whether it’s the US, whether it’s Europe, whether it’s, in different ways, or everyone in different ways. But I think when it comes to something which is far more than technology, because AI is far more than technology, AI’s power is geopolitics, is earth, cables, sea, so much.

I think one of the things that probably we are overlooking is how we, and if we will be, as a world able to come together and have some red lines and say, well actually, we’re not going to go

Thank you. So I just want to take a moment to thank the panelists and maybe I can ask Dean for you to sum it up.

Well, I think there’s a lot of different things. Unfortunately, the subject of AI governance is so difficult because it’s so capacious, right? It’s such an enormous topic. But look, I think we have a very real infrastructure development sort of challenge ahead of us. We have a huge complex of new types of institutions and old institutions that are going to change and evolve in various ways. And there’s all sorts of interlocking work to do on things like that that are going to be critical for the governance of AI for both everyday types of harms and also sort of catastrophic things that feel futuristic. But I think that are going to be real parts of our lives in the pretty near future.

And then I think, you know, another thing I would I would kind of double click on is this need for competitive. Yes, which I feel. agree with. And one of the things that I think is exciting about AI is that the price per token of models does drop quite quickly. And so there are a lot of good competitive dynamics here. There’s also centralizing tendencies. And so I think working together to figure out how to prevent those tendencies, I think that’s going to be extremely important. The concentration of power in AI and that issue in the long term is going to be, I think, one of the most important parts of the political economy of this topic.

So yeah, I think that’s how I see it.

Thank you. That’s fantastic. We spoke about trade -offs, we spoke about potential solutions, and we spoke about building AI readiness for national competitiveness. Thank you so much to all the panelists, and it was lovely having a conversation.

Thanks to the moderator. Thank you.