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The first question around India. One of the things you’ve done and your organization has done is you found ways of taking the power of AI, democratizing it, and making sure it solves problems that we all care about. In my speeches, I’ve talked about you. I’ve talked about oral reading fluency, the tool whereby for less than, and if I’m stealing your thunder, sorry, but he’ll tell you a little bit more. But I’ve been talking about it because it’s just amazing. I’ve been talking about the fact that your TB screening, you can do things that we couldn’t imagine being done before. So can you tell us more about your work in India?

Sure. Hi, everyone. Thank you. Welcome. Thanks for being here. Thank you for having me. I suspect the way I got over here was they needed, Gates Foundation needed someone for this chat. They looked around. They found this guy wandering around with two badges. They figured that means he’s important. Let’s get him. And next thing I’m sitting over here. But thank you so much, Ankur. So, you know, my brother and I launched the Badwani Institute for Artificial Intelligence here in India about eight years ago, 2018. Back then, AI wasn’t a thing. ChatGPT hadn’t come out. But I happened to be serving on the board of trustees of Carnegie Mellon University in the U .S., where I had studied, gotten my master’s.

So and CMU was ranked then as ranked number one in the world for artificial intelligence research and teaching. So being on the board, I could see all the billions of dollars coming into AI from Google and so on and so forth, even in those days. And it always pained me that none of this money was going into AI for society. You know, three, four billion people in the world out of eight billion don’t have access to decent health care, decent education. AI could be transformative. And that’s what we’re talking about today. But at that time, nothing was going on. So I spoke to my brother Ramesh. We decided, let’s launch this Institute for AI in India.

Prime Minister Modi came, inaugurated it, etc. So we hired a really good team of AI machine learning people, spoke to government, identified use cases, started working, and nothing happened. A couple of years went by, we were developing this, what we thought was really neat stuff, but it wasn’t scaling up. So we took a look at the issues, etc. And then we started realizing, look, we’re not approaching it quite right. We’ve got great AI solutions, but there is a lot more, a lot more to actually having impact than just having a nice technical solution. So I’ll, in a couple of minutes, tell you what we’re doing. So the key lessons that we’ve learned. But once we started figuring out, OK, what we were not doing and that we needed to be doing, then things started happening.

So just to give you two or three examples, as Ankur mentioned, we identify our problems, our challenges that we want to focus AI on by working directly with government. We talk to the health ministry about their national priorities for the next three, four, five years. What should we do? We talk to the education ministry and so on. So three years ago, the health ministry told us that tuberculosis is a very high priority for us. It’s the largest infectious disease killer in the world, kills close to two million people a year. Largest infectious disease killer in India kills close to half a million people over here. And for each person that dies, there are 20 others that don’t die, but they live miserable lives and they are infecting lots of other people as they go on.

So the government, the health ministry said, can you help? So we took a look at the whole. cascade of care in tuberculosis? What’s the patient journey like? Where are the three or four or five key pain points? And we identified, okay, diagnosis is number one, because in these economically vulnerable communities where TB happens, you need x -ray machines, you need sputum analysis, and in these communities, you don’t have all this stuff. You don’t have x -ray machines that work and are calibrated and so on. Problem number one. Problem number two, sputum analysis is another way of diagnosing TB, but these samples go to 64 government labs around India where they are ranulized, et cetera, and it takes time for the results to come back to the patient.

And for those that have TB, you’ve lost a lot of valuable time. Third big challenge is there’s a number of patients with TB who are on the medication regimen, but these are very toxic medicines. They really destroy your body while they’re trying to cure you of TB. So a lot of people stop taking these medicines. and they developed drug -resistant TB, which is much worse, 50 % mortality rate, etc. So we started applying AI to each of these issues. On the diagnosis, we’ve come up with a way of detecting tuberculosis from the sound of a cough into a smartphone. It’s instant. It’s quick. We don’t just say yes or no. We give the risk of this person having TB, what’s the probability, etc.

That is now rolling out nationally, and it is becoming the national standard. And by the way, we’re the only country that has this. It doesn’t exist anywhere. World Health Organization has told us this could be a game changer globally. For the sputum analysis, we’ve developed an AI model. So now the sputum analysis in the 64 government labs, totally automated. Results come out within a day, go back to the patient, treatment starts. Perhaps the most challenging thing. These patients who will fall off their medication. We’ve developed AI algorithms that predict well ahead of time which TB patients are likely to fall off the medication. So then the 2 ,000 TB caseworkers in India, which is a very limited number for 4 million TB patients, they can focus on the right people.

This is impacting now tens of millions of people. Just in the last year, the rate of TB detection, thanks to our cough against TB, has gone up by 25%. You may think that’s bad news, you know, higher numbers, but now we can treat these patients. We can get them on the right, you know, clinical care protocols. That’s one example. Education. Throughout the global south, there is a very high dropout rate of young children from schools, very high, in grades 1 through 5. Problem in India, problem everywhere. We got a call from a very large state government in India that said, we’ve got this issue, can you help? We sent a team in. We’ve got a call from a very large state government in India that said, we’ve got this issue, can you help?

and we had to analyze what’s causing this high dropout rate. We learned that the single biggest reason for this high dropout rate is an inability of these very young children, 7, 8, 9 years old, to be able to read If you can’t read, it affects how you do in every subject, right? Science, history, geography, you struggle, you start failing, you get frustrated, and these are, again, poor communities. Your parents say, forget school, what’s the point? Come work in the field or work in the kitchen, and that affects the rest of their lives. We’ve come up with an AI -based suite of tools that… …and for the child that goes to the teacher, and for the child, we come up with personalized exercises…

stories that they can read at home, but which help them to get better at their specific area of weakness. Each child is different. We were in pilot with the state. They were so impressed, they made it mandatory for all 3 million school kids in that state, in that age group. State of Rajasthan saw it recently. Here, in that age group. So that’s the kind of scale one can get. What’s the difference between what we were not doing in our first 2 or 3 years versus what we’re doing now? What we learned is, number one, the only way to scale is government. You have to work with government from day one. Working with government isn’t easy, right? It’s easy to say it’s challenging, it can be frustrating at times.

but you have to understand how to navigate it. How do you work with senior civil servants? You know, start with an, you know, approach them with humility, not like you have the answers. You’re trying to understand the problem. You want to work with them. Secondly, think scale from day one. You can’t develop an AI solution and then say, oh, I want to use it on one million people. There are issues you have to think in right in the beginning as to how will the scale out? How will large scale training happen in the field? How will frontline health workers or, you know, teachers or other governments? How will they use this? So thinking that way is very upfront.

And in fact, with government, what we do now is once we identify a problem, even before we work on the technical solution, we plan that deployment to scale as to what will happen. And we make government accountable for a lot of it. Just as we’re accountable for the technical side. The other really key learning has been. And that government, and this relates, Ankur, to what you were just saying, has developed a lot of digital public infrastructure. Aadhaar is like the great example that we’re all aware of, right? UPI, United Payment Interface, incredible example of that. So there are lots of things in health care, in education and agriculture where the government has developed this digital public infrastructure.

And it’s critical. We didn’t know this. This was probably our key finding. It’s critical to find a government platform that you can integrate into. So the examples I’ve given of TB, government has a wonderful platform called Nikshay. It’s like a case management system for tuberculosis patients. We’ve integrated everything. We developed algorithms into that platform. The education, this early childhood reading proficiency, each state has a platform. Rajasthan, as an example, has a platform called Rakshak. Rakshak for 70 ,000 schools, 400 ,000 teachers, 8 million students. we plugged our algorithms into that platform. So if these platforms hadn’t been there, we’d be struggling to scale any of this up. The final learning that we’ve had, and maybe this is the most important, is all these technical solutions are great at a macro level to bring down TB, to improve reading proficiency, etc.

But at the end of the day, if the person using this tool, the frontline health worker, the teacher, if it doesn’t make life easier for them, in addition to improving education for the child or healthcare for the patient, it won’t happen. You can push all you want from the top that, oh, you must use this, but there’s got to be pull. They’ve got to want to use it, and that happens only when you make life easier for them.

Thank you very much, Sunil. We’ll do the next question a little bit quickly. But I do want to just… acknowledge a few things, call out a few things. One is this journey between innovation and impact. I love what the learnings you talked about, because we keep on sometimes focusing on the innovation part, and we think that the road from innovation to impact is a straight road, and it’s not. It’s possible, it’s probable, but it’s not guaranteed, and we need to work hard at it. And so your learnings get to it. It’s also one of the reasons why we love our seven -year partnership, and hopefully it’ll be much more as we think about, as some of you know, the Gates Foundation yesterday announced a new initiative, a new pledge around AI for AI, which is Advantage India for AI.

And the idea is to make investments in India for the global south, and we’re looking forward to partnering with you. So, Sunil, one of the places where we do partner, and we’re talking about things, in fact, earlier today, we were talking about work in Ethiopia and Rwanda and Kenya. Can you talk a little bit more about… how you think about your work in the context of South -South partnership and how do you take the learnings you have from one place to the other place?

Sure. So when we got started, our goal was only India, right? My brother and I, we are from India, our hearts are still here. So we weren’t thinking about any place else. But what’s happened is as our AI solutions have been scaling up in India quite dramatically, and we are today impacting over 100 million people a year, we’ve developed over 25 AI platforms in partnership with government. We’ve started over the last year getting a lot of inquiries from governments around the world in the global South saying what you’re doing in India, we need in Kenya or in Rwanda or in Indonesia or Egypt or Mexico. By the way, in India, we don’t just develop these solutions. We also do a lot of capacity building, meaning training of senior civil servants on how you can use AI.

What it’s good for, not good for, etc. We help ministries develop data governance standards, use case frameworks and so on. Then we do the actual solutions development. That’s the biggest chunk of what we do. But then we have a big deployment team. We have close to 100 people making sure that these things get deployed. So we were thinking only India. But over the last year, we started getting all these inquiries. And we finally said, look, we set up this foundation to have impact globally. So now we’ve we’ve have we sent a team out to Africa to meet with several countries. We are starting operations this month in Rwanda, Ethiopia and Kenya. And I’m glad to see a colleague over here from Smart Africa.

We will be partnering in this work. We’re very excited about that. And then beyond that, we expect to be going to a number of other places today. As I said, we’re impacting maybe 100 million people in India. Our goal is by the year 2040. To be impacting 500 million people. We are very excited about our partnership with you at the Gates Foundation. So the Prime Minister Modi, if you heard him yesterday in his speech, he gave a brilliant speech. As part of that, he said he said for the last several years, I’ve been saying make in India for the world. He said, no, I want to add to that in this age of AI design in India for the world, develop in India for the world and then deliver these solutions to the world.

And that is what we’re trying to do. That’s the evolution now thinking. And again, we’re excited, very excited about the partnership with you.

Thank you, Sunil. Are we I think we have a change of plans. Thank you so much. And Sunil, if you could please stay on stage and if I could invite our panel up. Shalini Kapoor, Chief Strategist from the XSTEP Foundation. Lacina Kone, Director General and CEO of Smart Africa. And Shikoh Gitau, CEO of Kala. Thank you.

yeah good good to start okay thank you so much thank you anchor. Thanks for your time uh and here we are and thanks uh Sunil for you know spending some more time with us, uh thanks Shikoh we have been meeting bumping into each other and thanks thanks Lacina thanks for being here So we’ll get into some of the discussions on the South -South collaboration that is spurring innovation and that is diffusing AI into all the sectors. AI diffusion is about the routes and the rails which need to be laid in AI the similar way digital rails were laid in the DPI time. And now they can be shared. They are playbooks. They can be shared.

And AI diffusion actually concept came from it started with generation. Jeffrey Hinton talking about it. He’s a professor in Georgetown in D .C. When he talked about that, how actually electricity was created in. Germany, but it was diffused across India, across the USA, where USA made so much of strides into it. So like electricity, AI is a general purpose technology. It’s a GPT, which is there. And between invention and impact, there’s a big layer of adoption and diffusion, which needs to be there so that AI gets diffused into society. And when it gets diffused into society, something which can be built in Kenya can come to India, something which is built in India can go to Kenya, because there are playbooks which could be leveraged.

Not everybody needs to build everything. Not everybody needs to build the entire stack. How do we learn from each other? And how does that South -South collaboration happen? That’s what is the focus. So I’ll start with the pathways. What are the pathways to scale? and I’ll start with Lesina, that you are leading Smart Africa and you help coordinate across a lot of nations, different stages of AI, somebody in pilot, somebody in production, somebody has solved data, somebody has solved language, somebody has solved voice AI. What do you think are the opportunities for the South -South collaboration in building these pathways together?

Yeah, thank you very much for inviting me. In fact, that collaboration, first of all, before we even talk about South -South, the collaboration is a sense of the creation of a Smart Africa. Because if you look at Africa through just on the Kenya, which is 50 million, Ghana, which is 30 million, or Nigeria, which is 240 million, you’re missing the point. But if you look at Africa as a 1 .4 billion people, but be able to leverage that 1 .4, you need a collaboration. and the scale. So coming from that, when you look at the continent of Africa, which is technically speaking the global south, I don’t want to get there, in the global south, and the south -south collaboration is very important because we do not need to reinvent the wheel.

India has shown the world what the DPI actually means for 1 .4 billion people. It’s working digital ID, a country which is able to organize an election for 850 million people to vote. You have to actually kudos. So we don’t need to reinvent the wheel. Africa can learn a lot from India, even with the use cases. But why use cases particularly? Because there’s a lot of similarity between India and Africa in terms of culture, in terms of value, that we all know we are into the AI, we are into digital transformation. It’s not just… You create a… But the luxury of the luxury of the society is to be able to have a population, inclusion of the population, ethical of the technology, exactly.

So coming from that, that’s one of the reasons to reboost, actually, Smart Africa Initiative. The creations of the Africa AI Council came into play where last April, on April 4th, 2025, 49 countries came together to actually sign the declaration. And subsequent to that, the AI Council came to life on November 12th, 2025 in Guinea after our board of directors, which are represented by the head of the state, announced that, accepted that we could. We had a first meeting already. Then the council consists of 15 members. is not only driven by public, but it has seven ministers coming from seven different countries and eight private sector members. Why? Because in our constitution of Smart Africa, private sector first. We do believe that the government should be creating a conducive environment for the private sector to excel.

It cannot be dominated by public sector. And underneath of the council, we have, of course, six thematic groups, mainly computing, so we can look at the collaboration of South Asian computing power. We can look at it in the data set. We can look at the skills. We can look at the regulation, which is the governance. We can look into the market and we can look at investment. And when it comes in terms of investment, something we need to know. The investments in the prior technologies, the investment cycle is too slow for the AI. Just look back 12 months ago. Where were we? And where we are today? So this is something we need to look at carefully.

We are looking at the three aspects. One, the government needs to be creating a conducive environment for private sector to chip in. The private sector needs to be executing, but they can only execute, as I said, everyone’s cry because they said finance is the issue. I always said finance is not the issue. Finance is the last thing you should think about. You know why? Because I said financing is like the rain. For rain to fall, you need certain condition of the cloud. Those clouds are the regulatory environment, the conducive environment for business private sector because private sector does not like unpredictability. So the third thing, the philanthropics. The reason why I want to speak about it, the philanthropics need to serve as a 2D risk area because these are some of the things government is the last thing to invest in a technology because they want to make sure it’s going to work.

But if you don’t throw business, it’s time for them to accelerate, to use that as a de -risking while the private sector can chip in and so on and so forth. Thank you.

Yeah, thank you so much. And you talked about DPI, you talked about the private sector, public coming together. It’s the entire ecosystem. And on 18th, actually, Mr. Nandan Hillikini actually announced 100 Pathways to 2030, which is a clarion call. If you ask me, it’s a clarion call for people to join to create pathways simply because, you know, if you climb Mount Everest, suppose Edmund Hillary has climbed Mount Everest. And do you think he’ll come back and he’ll say, I’m not going to tell you how I climbed? What is the route I took? Where did I go? Where did I go? Where did I not go? What did I see? He will talk about, right? He will talk about them so that it is easy for other travelers to come in.

So pathway is like that. that if someone has done the AI pathway, others should learn from it, benefit from it. So, Shiko, you were with us on the stage when you joined and you said that, you know, from the Global South, 100 Pathways to Scale, you would like to join us. Please tell us, how can that diffusion help? How can this, you know, we can work, collaborate to, you know, to get the AI use cases from pilot to production?

I can finish? Okay. It’s that collaboration. As I was saying, this idea of how do we bring this multiplicity of thinking together, given that we have the same exact challenges. We have challenges around, we have multiple languages. We have culture and diversity. We have things that we need to be able to work together. How do we collaborate together? And for us, the biggest takeaway is how do we make AI not just a technology, but a political and economical issue? Yeah? That was the biggest one, because the people are there. The builders were there. The researchers were there. The policy makers were there. But we need that political goodwill to be able to make this work together.

And something that CV Madoka from I’ve forgotten the organization. CBC said that struck really a chord to everybody including myself is we need to start having a conversation on what is called the collaboration tax and it’s something DG when we were coming in we were talking about I’ll define collaboration tax as this effort resources and things that you need to put together to be able to collaborate with each other. It’s what the government should be doing it’s what that political part of AI should be doing to bring the collaboration together and how do we make these people come together without the effort of I mean not the effort, the pain of collaboration and that’s what we need to be talking about because the resources, the people are there people are willing to collaborate and work together we saw it this week and as the minister said I said while you’re chasing the Guinness World Record of having the most number of people also chase for the diversity that these countries are seeing thank you so much for bringing Africa and the world to India Yeah.

Thank you, Shikoh. Thank you so much. We’ll take a small break in the panel discussion and we’ll have Mr. Krishnan come here and talk about how scale and collaboration can help in the South -South and what is transferable from India. I know he’s like busy and across all. So over to you, Mr. Krishnan.

I was just going to do one more thing, which is thank you, Shalini, and thank you to the panel for allowing us this small break. For those of you who don’t know, Secretary Krishnan from the Ministry of Métis is over here. He’s had probably one of the most amazing, successful weeks this week. So please join me and give him a big round of applause for. Secretary Krishnan, thank you so much for being here. As a proud Indian, I’m quite excited about the fact what happened this week. I’m also, as somebody who cares about the agenda of this global South. everything that happened in India this week put the Global South agenda right front and center.

So thank you for doing that. There were so many announcements made this week about how we’re going to make progress in the months and years to come together. Would love to welcome you to give a little bit more context of the announcements that were made and the achievements that were achieved this week. Thank you. Welcome.

Let me first apologize to the panel for having sort of stepped in abruptly, but between juggling many things going on across the summit, I think this is a very important session as far as I was concerned, because if this particular summit was about one thing, it was about the Global South. The fact that… India representing the Global South… could actually dare to host this event and also dare to host it on this scale. The one thing we were very clear about is summits thus far have basically been about country leaders. It’s been about CEOs and it’s been about some experts getting together in closed rooms and not really having the opportunity to do what or to actually showcase to people as to what the possibility of the technology is.

And this particular event gave us that opportunity. I think we were very clear that what we wanted to do was to let people into the rooms. We wanted to make sure that people, especially youth, had this opportunity to come and listen to the best minds, there are on artificial intelligence as technology. and to every possible perspective on how this technology can work for everybody. And the second thing, of course, as you’re well aware, we said people, planet, progress. And two aspects of it were very important. One was, or three, if I can. One is democratizing access to AI, all the AI infrastructure and resources. That was one key aspect. The second key aspect was including those who are not ordinarily given access to this, those who are excluded.

And the third key aspect is putting humans at the center of this process to make sure that this is a technology that works for people. And I think the prime minister was very clear and emphatic in his address yesterday where he put people, or manna, right at the heart of AI. So to enable this to happen, of course, multiple things have to happen. We have to find frugal ways to innovate. In order to make these resources available, we have to make sure that we have the resources. We believe that our own AI mission model, the India AI mission model, is one of those frugal ways in which both the compute infrastructure and the model infrastructure and the data set infrastructure can be created for each country because some of this needs to be on a specific basis for regions and for countries.

In India, we are a subcontinental scale. There are 22 official languages and many other languages which need to be taught or which need to be understood. And we understand this cultural and linguistic diversity better than any other region in the world. And we can, at a continental scale, we can contribute in that effort. That is one key element. The second key element, of course, is to create compute in a way so that it doesn’t, I mean, people are not enabled. I mean, people are not able to build moats. around it. That, you know, the implication that you need the kind of resources to do this, that nobody else can do it and only we can do it is not an approach we wanted to take.

So we created this model where the private sector is encouraged to invest. We created this model where access to it is something which the government subsidizes. In the process today, AI compute in India is available at a third of the price that it is available in the rest of the world. I think that has been the significant achievement. The United Nations asked us saying, would it be possible to, once you build it out on scale, would it be possible to share with the rest of the world? This is something that we have committed to them saying, as in when the size is adequate so that we can meet other requirements, we will be happy to share them.

We are happy to share the model even now. And the AI Kosh model, as we call it, the AI treasury is something that we are happy to share even now. we are happy to share the fact that the models that we have supported in India and which have been built out as sovereign models in India that again is technology that we are happy to share with the global south it’s something we can enable some of it is something we’ve built with our own resources so it is in a sense completely sovereign unlike in many other places it’s something that the government has paid for from taxpayer resources and we can use and the third element of course is the data sets and how they are shared now that framework is again something we can certainly share the most important thing and I think that’s what is also showcased so eloquently in the expo is the range of applications which have been created out of this and there are close to 900 startups across all those halls who have done a variety of things even in the main hall we with the Gates Foundation we have set up a lot of applications and we have set up a lot of applications and we have set up the African village which is such a showcase even to the leaders fundamentally about applications which can work there and fundamentally for people to see applications which have worked in different parts of the world which can be taken elsewhere.

So all of those are available. Those are resources which we want to share. These are resources we want to actually give. And most importantly, as I said, I think if there is one thing that this summit reflects, for the first time, we’ve actually democratized AI. We’ve shown you what democratic AI looks like when people are let into the rooms, when people are let into the halls and they can see for themselves as to how this would work. So it gives me immense pleasure that, you know, and a very, very key partner for us in all of this has been the Gates Foundation right from the very outset, right from the planning stage of how we wanted to do this.

This particular part of the set of sessions on the Global South is something, that we work closely with, curated carefully. We have put together sessions which will be relevant to this group, and we have always made sure that in addition to this, of course, on every occasion, whether it is in the space of DPI or whether it is in the space of any of the other applications, we are in a position to support it. Under one of our organizations, the National Institute of Smart Governance, we have now put in a center which is fundamentally focused on international cooperation so that they can actually provide support to other countries where DPIs are to be actually implemented, how to ground them.

And we believe that probably the most effective way of dealing with this is to actually be able to cooperate amongst ourselves. So. So that we are able to take it out. We are able to learn from each other. We are able to contribute to each other. And that is something we are now really ready to do. India knows what it is to be deprived of or denied technology. India knows what it is to actually try and work your way past it. We have managed to do that. We have managed to democratize it. We have managed to make it available to people at scale. We have tried to keep it open source. We have tried to protect it in a number of ways from cyber attacks in each of those areas.

So in this entire technology stack, there is experience. There is the way that we leapfrog different stages. So I think if we work together in the AI space, likewise, there is so much that can be accomplished. And we undertake as a nation, I think I can say with responsibility that we will have devices and we will have structures through which we can sort of deepen this cooperation. We can deepen the support. We can enable this in a number of ways and continue to stay engaged through the Gates Foundation, through the other institutions to actually make this happen. So thank you very much. Thank you to Gates Foundation for curating this particular event. And thanks to all of you for participating in this.

I mean, it’s one thing to arrange it, one thing to organize it. But another thing for all of you to actually come up here, put up with some of the inconvenience which would have been caused. India is not a very convenient country at best of times. but India is a country with spirit and India is a country which fixes things

Okay, we have some time with us. How much? Five minutes. And we need to have a question to Sunil. He has been working at, you know, so many interesting, I mean, I listened to the stories of Wadwaning AI. and they inspire you thoroughly. So Sunil, I’ll give it to you. I want people to hear your message that how can these work that has been done in India, how can it help Global South?

So as Mr. Kone of Smart Africa said 15 minutes ago, the challenges that we have in the Global South generally, really Africa, India, other countries are similar. The values we have, more importantly, are very similar. The strengths that we bring to the table in terms of our talent, our youth, et cetera, are similar. So I think it’s mutual learning. It’s not one way. It’s not that we’ve developed great stuff in India that can just be, you know, taken over. It’s mutual learning on both sides. It’s a mutual sharing of ideas. There are lots of very good things happening in Africa and Asia. There are lots of things happening in Africa and Asia that we can learn from over here.

On the technology side, as you were saying, Shalini, we’ve been fortunate. We’ve had a government that is very pro -technology. There’s a tremendous range of digital public infrastructure that we can access in India that enables, that provides data pipelines. It provides digital distribution systems without which none of this AI can scale. There’s been a very clear regulatory framework for AI that’s been developed in India that really helps. And most important, there’s an openness in government. And I think it’s driven by the prime minister’s vision and belief that technology and AI can truly transform societal development. So those, to me, are the big things, more than individual AI solutions that really make a difference. And I see that happening in Africa in many countries.

Sure. Thank you so much. I think we are literally… We are literally at the eve of the summit getting over. It’s been a fantastic week. meeting the best of the people, listening to the best of the sessions, navigating the traffic, yes. But like Secretary Krishnan said that, you know, we fix everything. So I just want to have one last question to each of the panelists. What’s the best thing you liked of the summit? And to you, Sunil, first. I mean, one moment, one feeling that you will carry forward.

I will give you a counterintuitive answer. AI is making the world move faster and faster and faster. And all the traffic challenges we’ve had over here are teaching us patience. You will get there. Things will happen. Life will go on.

Thank you so much. Shikol, what’s the one feeling you’ll travel back with, back to Africa?

I think my best moment is, and I’m going to pick and be selfish, in the moment in Oberoi when I stood and saw this diverse sea of faces, I think about 300 people, and we’re all celebrating like, we can do this as the Global South. This is happening in our TAF, and this belief that the Global South has something to offer into this AI conversation. So it’s no longer a two -horse race, it’s a multiple -horse race. Thank you.

For me, it’s, you know, our vision is to transform Africa into a single digital market. Coming here this time, it shows me already our future. Having 1 .4 billion people on the one regulation. How does it feel like? So you know what I’m talking about. That’s one of our obstacles, says regulatory harmonization in India, you do not have that. multicultural, you have a multicultural multilingual, you have multicultural what does it feel like, including the traffic in the morning as well of course thank you

And my best moment for the summit was that back in Oberoi on 18th evening, several partners across Italy, Kenya Anthropic, Google Carnegie ORF, Gates Aikstep stood next to Nandini, Kenya and we all came together for 100 pathways till 2030 and we all were together and we were not doing non -collaboration pictures which is going on in Insta, we were all together so it was AI is about collaboration not competition, that’s the theme thank you thank you really enjoyed thank you thank you thank you you are the best man you are the best it’s a pleasure more photos more photos Thank you. Thank you.

Key area should we think about it as a governance data governance problem, problem in model design or should it be more on a verification or compliance angle.

Thanks thank you very much Shyam for having me and good morning to everyone and welcome to this session. So I think okay let me maybe just start with saying that I’m not an AI or AI safety expert so whatever I say take it with a pinch of salt. My work is in biosecurity and that’s the angle I’ll come from. I think all of those things whether it’s a model evaluation and other things those are there and those are very very important factors and that those are the things that we need to keep in mind. But on top of that there is also a very important deep structural change that is happening. For example in the field of life sciences historically whatever risk and risk governance things that we had were very much linked to the physical infrastructure and lab facilities and facility inspection and material transfer control and things like that.

But that seems to have changed and seems to be changing very rapidly now with the kind of AI biodesign tools as well as LLMs that are emerging. So I think Rand also did a study on this, but there are more than probably 1 ,500 biodesign tools that are out there, and those are totally transforming how life sciences, but in general, science is done. Now, what kind of change that we are seeing is with these capabilities, now it’s much easier to engineer proteins, optimize DNA sequences to do things that we want, have better pathogen host modeling, interaction modeling, and things like that. Now, these capabilities are… because of AI becoming partly decoupled from the physical containment measures which were usually used in the life sciences.

So we have a lot of this risk landscape shifting a little bit more upstream to the design side when it comes to at least biological side of things. So yes, data governance, things matter. Model evaluation and red teamings are essential and we should be doing that. But also it is very important that especially for a country like India where we have a very vibrant scientific ecosystem but that is also very uneven. How we can use this AI -enabled science which is rapidly evolving into the existing mechanisms to some extent but also at the same time develop those capabilities, have more people with the core capabilities and more people with the core capabilities and more people with the core capabilities chemical security, AI nuclear security, and things like that.

So we need to train more people on those things. So integrating, again, going back to the life sciences, so integrating AI evaluation into biosafety system, strengthening the institutional readiness. Some places there are information, some labs and some institutions have information security labs or information security offices. How we can get them better prepared for these new emerging risks that are coming due to AI. Some places they have biosafety officers or biosecurity officers. How we can enable them better to address the AI risk is what the direction that we need to move towards. And have more adaptive oversight mechanism that is not only based on the, limited to this once in a while inspection that happens, but that goes more with the rapidly evolving things that we are seeing with the AI models coming up.

And I think, I think, So, just in terms of paradigm change that we are seeing and that you mentioned, is that there need to be more decentralized checks and balances and oversight mechanisms. If there is one authority sitting somewhere in Delhi and trying to do everything, that’s not going to work. So that is one of the things that we have to collectively think about. How do we decentralize these kind of oversight systems to some extent? For example, as I was saying, how we can empower the information security or biosecurity offices and create what in the field of disarmament where I have worked on called way of prevention. One measure is not enough. It’s not sufficient.

You need to have a number of measures in place which collectively can help prevent something bad from happening. Thank you.

Thank you. That’s very insightful. And I think we’ve already touched on some areas that, you know, that would be follow -up questions. P .T., focusing a bit more on… open science where high risk domains, especially in biological data and AI capabilities, as Surya was mentioning. How do we preserve the benefits of open science while preventing the destabilizing diffusion of capabilities that we were just discussing about?

Thank you. Thank you for having me today. So I guess like I would love to be able to give like a binary yes or no answer. Right. I think we all want to have that. But unfortunately, that’s not quite the case. So we need to find a way to balance the openness and also the restrictions as well. So I guess my answer here would be sort of like a tiered access and contextual norms. I think those are really important. And I think RAN Europe has done a really great job at establishing the global risk index on AI enabled biological tools. And also just generally looking into AI safety in general, where they do this thing where they call the pre -deployment assessment.

with structured rubrics. And I’m a huge fan of that because I think that when you release very frontier models and frontier tools, the danger is already out there once released. It’s really hard to withdraw the danger. But however, prevention, right? There’s this window before you release where you can do a pre -deployment assessment. So I think I’m a really huge fan of that and also the same way that I’m a big fan of KYC, know your customers. And I guess this principle also pretty much applies whereas in the case of biosecurity, where we differentially allow the development of medical countermeasures and also the defensive measures that is necessary for the research, but also don’t limit the researchers from actually innovating either.

And I guess my point here is that we’re not going to be able to do that. The non -safeguarded access, like private access to credential researchers where necessary for like defensive research is absolutely necessary. And then, you know, like open source tools, it’s necessary. Like we can’t turn away from being open source. Like any governance structure that conflates open source with danger makes a huge mistake because that also is a very critical development point, especially for lower resource settings. So we cannot afford to conflate that altogether. So I guess a very long way to answer this and then to summarize my answer is that differentiated governance at capability level is always better than blanket restriction at access level.

Yeah.

I think that’s a very structured answer and I think, you know, there’s a start of a very valid framework level conversation that’s already happening there. Geetha, turning to you, thinking more about institutional gaps in enabling some of the solutions that we are discussing, potential solutions, what are the most immediate gaps that you see in evaluating systems, technical capability, regulatory and coordination, largely from the policy angle that you work in?

Thank you, Shyam. Good morning, everyone. So on the technical capabilities, right, the most fundamental thing I see is the AI readiness aspect of deployment. So in general, when we see India stands or ranks third globally, and when we see the Southeast Asian countries, I think Indonesia is around 49, and so there we see the gap, right? So whatever we do from the Western context or in the Indian context can never be catered to the AI readiness aspect of deployment. So I think it’s important to the needs, the unique needs of the Southeast. Asian countries and moreover what there is the end user perception where we see that we have to build lot of capacity for creating awareness among the end users who are actually going to use the products and from the policy perspective I would like to give you certain aspects where we think about the socio -cultural aspects that is relevant to the deployment environments.

So in general the large language models are usually trained on the western data and the very recent research work maybe I will cover a bit of both tech and policy here. So there is a Southeast Asia related benchmark, safety benchmark which says that all these leading large language models have failed when they evaluated for more than 20 to 30 percent of the risk. So in the biological settings so which means that we did not have enough safeguards which will protect people from encountering all these risks. And moreover, so this lets us know that we have to build in more sociocultural evaluations and assessments which will cater to the harms that is more particular to that particular deployment environment rather than just having a high level evaluation strategies.

And this cannot come just from the policy side, right? So we need to bring in all the participatory approach which will bring in the end users, the different stakeholders involved in using all these AI systems, be it model, right from the requirements definition, right? So when we assess whether we need an AI system or not, generally now there is a perception saying that for whatever we are going to build or the problem that we are going to solve, by default we assume. We assume that we need a large language model which will not care. which is not even possible to have it deployed in a low resource setting, right? So we need to think about small language models which will enable edge deployments at the low resource settings and also consider all the multicultural and socio -economic diversity that exist in these regions so that your model doesn’t hallucinate, is still fair and also establish some governance and accountability frameworks which will make the developers more accountable and also because having the developers more accountable will enable them considering more safeguards, right?

And also create more awareness about the main fundamental thing is that they will be expected to document whatever testing that has been gone through. And on the policy side, there is one more aspect which is the Indian government also endorses, right? The self -regulation. voluntary commitments on managing and mitigating risk that comes out of all these AI models. So I think we have to have a unified framework which can still be adaptable to different deployment settings.

I think we are already getting a diversity of perspectives here and it is very useful to hear. Moving ahead and thinking about institutionalizing these kind of capabilities in scientific AI context, PT turning to you. Should independent evaluation and red teaming of AI systems from a technical kind of solution perspective for this problem that generate biological outputs, especially thinking biosecurity, given your perspective on this, should it become a norm and part of the global scientific specialist infrastructure? And if so, how would we go about that?

I think we have to have a clear understanding of the role of the AI system and how it can And I think that is a key point. And I think that is a key point. And I think that is a key point. And I think that is a key point. And I think that is a key point. And I think that is a key point. And I think that is a key point. And I think that is a key point. So I guess a good example to use here is probably we’re thinking of nuclear weapons, right? Which falls under this organization called the International Atomic Energy Agency, the IAEA. Now, from my perspective, I think fissile materials, correct me if I’m wrong, they’re very scarce.

And they are, to a certain degree, technically trackable. And they are also, more than anything else, highly regulated. Whereas biology, on the other hand, is everything but that. It’s diffused, it’s dual -use by nature, and it’s also nearly impossible to trace. And also, most importantly, commercially available, right? And so in the recent study, actually, this was done by this organization called SECURE. Bio, where they actually tested frontier large language models against expert virologists. And it turns out that ChachiPT -03 actually outperformed expert virologists by 94 % at troubleshooting wet lab protocols. So that’s a very shocking number, right? And then, I mean, obviously you mentioned earlier that there’s a very concentrated effort that is happening between the US, UK, and China, like the global superpowers, basically.

And I guess there’s, we, in the recommendation from the RAND Europe that I was, you know, helping out with is that we recommended that governments and also independent researchers do this six -monthly ritual of monitoring and also assessment of risk on a continuous basis. And we also suggested, obviously, like using AI as an automation tool to increase the efficiency of this risk monitoring system. But I think, to your point, I think stuff like this, stuff like that is non -interactive methodology that doesn’t require, you know, researchers to actually query directly with the danger systems is actually already in and of itself a very meaningful, you know, safeguard. But that is not enough. You know, we need something that is much larger than that.

That is the integration into, like, you know, institutionalizing it. And I would argue that, like, a six -monthly, you know, ritual, that refresh cadence, for it to be delivered, it’s going to require a very significant investment from the government at multilateral level, right? And so we can’t go without any investment at all. So my suggestion would be to actually implement this AI safety or security institute model that we’ve been applying where largely… It is technically credentialed. It’s independent, but also has a very… formal relationship with the government. And something that I would caveat from the bio side is that for the institution to have some kind of anchoring around biological weapons convention or the WHO.

Because right now that relationship is not quite there yet. And I think, you know, back to my point of like pre -deployment assessment, I think that is definitely needed and then the result has to be shared then across the credential network with tiered confidentiality that rather than being kept, you know, as a proprietary to the different state. I think it’s kind of a

That’s an interesting position, PT. Suryesh, thinking more about safety measures at large, how can we make sure that they remain rigorous and feasible within research ecosystems that you’re quite familiar with, you know, from a biosecurity angle, if you will, but largely also in the larger scientific ecosystem.

Thanks Shyam. I think first, yeah first thing that we need to understand is how that ecosystem is and then see if certain measures will work there or not, right. One of the hallmarks of let’s say Indian scientific ecosystem is there is a lot of heterogeneity. There are some places which are really extremely well performing and there are other places who are not well resourced or have other all kind of challenges. So, understanding how the ecosystem is, what kind of regulation within the institutes that are there, what kind of administrative measures that are there, what kind of safety teams these kind of institutes might have, all of those things are extremely important, right. The governance capacity, compliance culture and technical expertise varies widely in Indian institutions.

And I believe this is true for many other countries in the global south as well. So it’s not something very unique. Particularly to India, we have challenges related to different kind of resources. And even when the resources are there, sometimes it’s also problematic to use them efficiently enough. Now, given that context, if we just import safety frameworks that are developed in a well -resourced place in a Western country or any developed countries, I don’t know if those would be a very good fit for the kind of system that we have here. So those might become more performative than functional to some extent. Another challenge that also P .T. mentioned to some extent is that the speed and scale of AI is huge, right?

And we need these traditional review mechanisms that institutes have for safety audits and all of those things are not going to work. We need something which is far more adaptive and quick. And also what we had traditionally is this periodic paper -based facility -centric kind of measures. And those are very much outdated in the era of AI that we live in. Now, so what… Now the question becomes, how do we design proportionate capability -aware safeguards that would be better matched for the challenges that we have? One of the major challenges, as I think a lot of us realize, is that there is limited awareness about AI safety when it comes to scientific issues, even among the scientists.

So a huge number, a large majority of scientists just don’t know what they are putting, let’s say in chat GPT might be harmful or what they are getting out of biodesign tools could be harmful to some extent. So there is some understanding about the privacy -related issues, but safety and security is still a big gap in understanding of even the scientific experts that are there. Now also regarding AI, I think there needs to be a tiered risk classification. So not everything is highly risky. There are certain biodesign tools, for example, that are trained in… in virus data. Those we’ll put in a higher risk category compared to something which is just working, let’s say, on certain animals which are not dangerous.

Now, also the safety measures, as I was mentioning earlier, as the risk has moved a bit upstream, it has come more on the design side, we should also have more safety measures moving upstream. And as Piti was mentioning that, you know, certain kind of evaluation that are before launching AI tools are necessary, but also integrating AI evaluation modules into grant review processes, creating cross -trained AI biosafety review panels, so panels specifically for AI biosafety at, from the bottom -up side, instead of having them from the top -down approach. Investing more in domestic evaluation capacity, having more AI safety institutes like Geeta’s home institute at IIT Madras. So we need a lot more of that. And lastly, I think what we have in the US and UK are these, a lot of AI safety work is being done there, right?

And as I was mentioning, importing that directly might not work. And we in the global south are largely the users and importer of this technology. So we have to see from the bottom up side, where do we put those safety measures? Do we, like when it comes to import, what kind of, when the data is being transferred, is there certain places where we can put those kind of safeguards? Also, how we can use some tech sovereignty measures in this context, right? That tech sovereignty measures are used for a number of things, but AI security is something, AI safety and security is something where those could also be used to some extent. So, yeah, I would stop here and then we can discuss.

Thank you.

Thank you. And I think a lot of useful thoughts here for us to explore a bit more. I think we’ve… just crossed the mid mark and I’m going to use Geeta to kind of like bridge between the next two topics by combining two of your questions sorry for that so just as Surya just mentioned will the emerging scientific powers you know global south middle powers would they be able to shape governance in this context especially you know enable science or will they continue to inherit the frameworks and if they were to show leadership what would that look like in scientific AI and research ecosystems and you know you’ve already been working on some of this so I’m looking forward to kind of hearing concrete measures that you know are happening

Sure. So in general what I think is definitely the emerging powers right they are putting on all efforts to bring in all the tools and frameworks that are required for governing these AI systems and for example, so India’s strategy towards all these emerging techs is that they are trying to create sandboxes which are highly essential for deploying or evaluating safety aspects for the models, right? So they do it for healthcare systems, they do it for ideology systems and whatever, right? So these type of tools and frameworks come from Indian settings will actually help the other underdeveloped countries to learn from the strategies that we use and then build something of their own or something which cannot go cross border can still happen through learning and collaboration, right?

So for example, we are going to launch a global south network for trustworthy AI and we are going to launch a global south network for trustworthy AI and we are going to launch a global south network for trustworthy AI which will enable all these mechanisms to happen, enable people to… develop and deploy AI systems which will be deployed in the low resource settings. And the other initiative which is going to give a very big leap in evaluating AI safety is coming up with an AI safety commons for the global south. That is part of the safe and trusted AI pillar that is one of the pillars in this impact summit and I think in another one or two years we will have safety commons which will help us evaluate and assess how these AI data models and systems work for different deployment settings.

Another important thing is that as Suresh mentioned about the audit frameworks. So when we come with, when we focus on the kind of risk and audit mechanisms that we have here, we still have it from an organization perspective and not from the end user perspective. So at CRI, we have come up with an incident reporting mechanism and a framework that caters to the Indian settings. So it tells you how to operationalize incident, AI incident reporting in the Indian settings, which is completely different from the Western settings. And here we have to get the harms that the people experience in the marginalized communities, which will never be recorded everywhere, right? So how do we enable all these things?

So since it is all about all these CERN -based systems, right, even those things will have certain impacts to the marginalized communities, which may be an indirect impact. But how do they are knowing about such things are happening to them, right? So those kinds of gaps we should mitigate by building more awareness, creating more AI literacy. And we should also be able to provide more privacy to all these people. The final thoughts about combining all these things is that we have to bring in some kind of collaborative work between the different stakeholders who are involved in developing and deploying these systems. And the governments have already given certain prompt knowledge about how to enable all these things through the techno -legal framework and guidelines that was recently published and the AI governance guidelines.

Which was recently published by METI. So the Southeast Asian countries can learn from the developing countries like India and then have curated a more tailored approach towards their unique needs. So that is what I think. So whoever has an opportunity or a willingness to have more things that will actually help them use or leverage these technologies can learn from whatever. Learn from. the mistakes as well as the experience that the other countries have, which is now openly available through all these summits.

That’s very useful and I’m looking forward to following up on IIT Madras’s work in this front as well. Going to Suresh for kind of the last question in this series really, should, you know, safety measures, evaluations, primarily focus, where should the focus be at the model level? And you talked about upstream quite a bit. Should there be more broader socio -technical readiness measures, misuse considerations? Where do you think it should be?

And also, very importantly, how we have to also see it from the context of, you know, people doing their own thing, DIY kind of science that happens. And also, small -scale commercial activities which are not fully under the oversight mechanism of the government, right? So, considering all of these points, right, the policy evaluation must expand from model -centric assessment to socio -technical assessment. And this would include, you know, evaluating things like how much capability uplift relative to the government capacity that is there. So, government has certain capacity to manage or do oversight, but these AI tools, how are they changing that? Incentive structures, very, very important, that shape the model deployment. Also, the diffusion of risk across borders.

All of these things don’t respect national borders, right? So, how it’s going to spread. If people using VPN or other things, a number of other things that are there. So integration, lastly, the integration with existing biosafety and resource security systems as I had already mentioned. So briefly, like performance evaluation is necessary, but governance -relevant evaluation must be systemic. And otherwise, we risk auditing algorithms while ignoring the institutions that operationalize them. And that is very, very important, how we focus on that institutional level mechanisms. Thank you. Thank you.

Piti, kind of the last structured question before we move into a bit more of an open conversation. AI becomes embedded not just in new capacities, but also existing programs like biosurveillance, public health systems. And so there’s a mix between emerging kind of scientific knowledge with more legacy, let’s call engineering knowledge as well. So. So how do we make sure that safety, evaluation, interoperability, all of that exists in this divide without fragmentation happening across the ecosystem? Because, you know, you can easily imagine everyone’s doing their own AI, you know, safety evaluation and not necessarily talking to each other.

Thank you, Shyam. I think this is a very important question. And it’s also a topic that I’m really passionate about as well, which is biosurveillance. To your point, I think, you know, countries are already deploying AI -enabled biosurveillance systems that are, you know, either syndromic surveillance or it could be, you know, genomic sequencing pipelines or outbreak modeling. The countries are already doing that, but they are not building on… the unified data standards. So they’re basically building on very incompatible data standards with very different legal regimes across the borders. We’ve seen that in Southeast Asia. We’ve seen that even countries like, for example, Singapore to Malaysia, you see different legal regiments on how they monitor the data and also the biosurveillance.

And so the fragmentation risk is actually not a technical risk, I would argue, because it’s not just a technical risk, because we’ve seen COVID. I feel like if anyone is anybody saying, I think we all were a little bit traumatized by COVID. We’ve seen how data hoarding and incompatible reporting actually cost lives. And I saw that especially happening across the region in the lower resource settings. Like countries like Cambodia, for example. AI systems that are trained on non -representative data obviously are going to perform much worse. And guess what happens? When they perform worse, the region that is most affected is the region that needs the help the most. And because of that, and also that region is also the same region with the least data infrastructure.

And so I guess to sort of like answer your question and what I think we need to do, I think there are three things to be addressed here. The first one is obviously the data standards harmonization. Currently, we don’t have that. I think we would need not like a global overhead standard that enforces on every country, but more of a federated interpretability that applies frameworks that applies to different countries. So I can think of like HL7FHIR, which is the federated… healthcare interpretability resources that are attempting to address these very specific issues on clinical data, but this one would be adapted for public health surveillance. And the second point is the legal safe harbors for basically just kind of cross -border sharing of data for public health emergencies that are negotiated beforehand because, and this is important, beforehand, because if you negotiate during an outbreak, people are going to be freaking out.

People are going to be like, I’m not going to share my data to you. What are you going to do with that data? So this needs to be done beforehand. And the last point, and also the most politically challenging point, is actually to have some kind of shared evaluation criteria across the board between different countries that are embedded into the national surveillance systems. And, for example, like Singapore data infrastructure environment might not apply to countries with like different climate data or like different demographic data. So this needs to be applied into, you know, the national surveillance systems. And what I noticed, I guess like the last message is that what I noticed the AI governance framework often thinks of biosurveillance as like an edge, like a niche edge case.

And then people in biosecurity frameworks, like doing biosecurity frameworks, thinks that AI governance is like a tool. And these people don’t talk to each other. And that gap, that gap right there is where the risk happens. So, yeah, we just need to talk to each other more. That’s easier said. Yeah.

So I think I’m just about to close with maybe five minutes or just under that for audience questions. Thank you, Justin. 10 second final thoughts on each of you from the panel. Suresh.

Just wanted to very quickly, we need to also keep in mind that how AI could help solve some of these AI safety challenges. How agentic AI could be used, let’s say, when people are trying to develop vaccines. CEPI has developed this platform where agentic AI is being used to check if there is someone who is trying to jailbreak or someone who is trying to misuse the tool that is there. Second very quick point, also, with all that what I said, there is still a gap to transfer things from digital to physical, what is called digital to physical barrier. So, even if you have everything, you still can’t just develop, modify viruses without having a proper physical infrastructure and there are still some ways to control that.

Thank you.

I think we should move on transforming from issues to intelligence like learning from the risk that happens and feeding it back to the model training and other assessment activities to mitigate the risk in real time so that is where we need to move towards bringing in more people into evaluations and then making it safer for people to use

I guess I’ll make it quick the point that I want to make here is that Shurya should echo his point I think you’re right that we should not shoot ourselves in the foot especially for developing countries, I think it’s really important and so my message for the last message here is just kind of like while we are forging ahead in innovation and while we are innovating ahead in whatever domains of scientific domains that we’re doing we need to be conscious of the impact that we have and I think in the AI Impact Summit is one of the really good places to jumpstart that kind of conversations and break the silos. Thank you.

Thank you everyone. I’m just going to take probably one minute to kind of summarize key points Evaluation, I see largely a systemic question, safety measure systemic question. I especially like the point on incident response not being already there. And a couple of points on the cross -border solutions and problems, we already have that. Discussion on open signs, we talked about how managed access, safeguards, and comparing government capacity to manage that versus letting it out for more DIY -oriented signs, which is a good term, I really like that. That’s a key area. And for emerging scientific powers, of course, collaboration is key. Tailored approach, that’s something that I’m again waiting to see from IT Madras as well, their contribution on this.

And some cross -border work on legal safe harbors, data standard harmonization, PT that you mentioned, really land well from this panel. I’m going to… I’m going to stop my… summary right now and you know more of this would be kind of put together in a blog at some point in the uh nearby future uh perhaps uh we can go for questions uh first uh yes please i think i can give you mine

Thank you so much for your wonderful insights i really enjoyed this session as a researcher in safety of ai at the university of york so i focus on psychological harms of ai and so what i want to ask particularly gita is um when it comes to the definition of harms and traditional safety engineering they’re catering more to physical harms and now we see the whole spectrum of harms expanding beyond that so i would love to know the work being done by karai and you in this area and and in fact enrich my research with it

Yeah, sure uh so when we actually assess harms and impacts right we do we have to do it from the different two different perspectives one is on the functional side where we assess all these algorithmic risk and other stuff. From the human centric perspective, like you said, we can keep doing everything from the psychology perspective and other ethics and other stuff. So, here at CIRI, we do work on assessing bias, determining whether the model is stereotypical or not and how do we generate explanations for the high level scientific models and all. So, from the perspective of the psychological things, there is this cognitive science or cognitive capabilities of AI models which will actually enhance or degrade the capabilities of humans.

So, those things are we are trying to do some assessments from the incident perspective. So, if you go to read the incident reporting framework that we have, we have a taxonomy of risk and harms and also the impacts. So, from the kinds of harms that we have defined, we have categorized it as physical, psychological, cyber incident based harm set. And moreover, we have all the generic kinds of harms like algorithmic harms, socio -economic harms, the environmental harms and all. So, we are trying to come up with a taxonomy that will cater to the different hierarchies that will be applied to these kind of harms and impacts which will again be model specific, use case specific and the domain specific.

So, that is where we are trying to work on. And we also have a healthcare based tool, a toolkit which will enable people to actually assess the perceptions of how they treat these models, how they see whether these AI applications are helpful for them or not and then come up with some capacity building programs for different roles in which they are working on. And this has been done with CMC Wellure Hospital and we have been assessing the perceptions of healthcare workers. and then come up with a training module which will enable them to use AI models or tools more confidently rather than, say, being resistant or not relying on them for so much.

Last, probably last quick question. Maybe keep it short on the responses as well, please. Sorry.

Hi. So my question is about, like, we are discussing all the geographical barriers, right? The modality is geography. When we change the geography, the models tend to perform poorly. Are we concerned about the temporal modality as well? When we go forward in time, the data is going to change eventually, and that is going to affect modeling. And how do we plan on, like, you know, mitigating such a problem if it arises?

Yeah. So this comes under the model monitoring, the system monitoring approach, where we consider the data drifts out of distribution. So we consider the distribution aspects of the data and models. So definitely this is one of the criteria where you assess safety and evaluate the impacts of it.

Yes, I think last question

Thank you so much for the insightful discussion, really appreciated the expertise that you’re bringing to the topic and thanks PT for bringing up COVID because my question is about that. As we learn from COVID biosecurity risk can quickly become a cross border existential threat. So what would a successful web of prevention and incident response framework look like and who are you looking up to in this space? Like who’s doing it well in this space?

I can start maybe PT can add. So I think as I was mentioning, it will have to be more decentralized but at the same time integrated to the leadership. So I think there needs to be more empowerment of people who are like biosafety officers in the lab or who are institutional biosafety committee members, who are people who are working on the ethics and research security side at the institutes. So those are the people who need to be empowered. So there needs to be more capacity building of those people and at the same time there needs to be a mechanism established so they can report those incidents to the very top and there is top leadership sitting in the capitals.

They can in some way get an overview or monitor the situation as it is going on at different institutes level.

Thanks. I can add a little bit to that. So in Singapore we actually have different agencies responsible for this. So we have the National Environmental Agency and then we have the MOH, obviously the Ministry of Health and then we also have different smaller agencies like Communicable Disease Agency and also like Prepare Agency where they are responsible for different tasks. But I want you to envision this as almost like the way that Singapore is trying to establish itself. I think it’s trying to establish itself almost as a firefighter. So when there’s an incident where there’s a crisis, who is actually doing what is very clear but it’s always not always clear across like different countries. For example, in Laos, Vietnam, might be looking very different, but I think having a very coordinated response across the different agencies on who is doing what.

Like, for example, National Environmental Agency is responsible for wastewater surveillance. So monitoring how the sickness is increasing or spiking or not, those are the people, yeah, that you would look up to. And I think that’s the last word, right? It all comes down to prevention and preparedness, even in this much like anything else with biocontext.

Thank you, everyone, for the question, and thank you to my brilliant panelists, Suryesh, Geeta, and P .T. This was a very insightful discussion. On the screen is the work from RAND Europe with CLTR, some of what was referred to by P .T. and other panelists as well, some aspects of what we were discussing about risk typification. You’ll probably get some ideas there as well. And with that, I close. I’m surprised. I’m supposed to hand over these mementos to apparently including me, so let us do that now. Thank you.

Institute. Then we have Raphael Leuner, Data Scientist at the German Federal Foreign Office. We have Dr. Shahani Yaktiyami, Senior Officer, Technology Program at the German Marshall Fund. And we have Norman Schulz, Consulate at the Coordination Staff, AI and Digital Technologies at the German Foreign Office. And to kick off the conversation today, so we will cover both AI as a topic and as a tool, I would like to first start with a tool. So going to Raphael, who is a Data Scientist, how do you use AI in the Foreign Office? And I also know that you have data labs, data and AI labs in the Foreign Office. So could you maybe share a little bit of your day -to -day work?

And yeah, actually, how could AI be used in diplomacy?

Yeah, thanks so much. Yeah, maybe to get to take a step back and answer the question, how like someone like me as a as a data scientist by training ends up in a foreign ministry. I think that’s something that at least when we talk to colleagues around the world is still rather rare. We had kind of the lucky coincidence that I think in 2021, the German government decided to start data labs in all of its federal ministries. And so in the coming years since then, 2022 or until 2022, kind of 16 data labs have been founded in the German federal government. And I was lucky enough to be part of the one in the German federal foreign office. Yeah.

And I was working on AI ever since we started more on traditional data science, I would say. So tearing down data silos between governments or government institutions, in Germany and, of course… And ever since JetGPT and the AI revolution, we have been working mostly on AI tools. And I think the big advantage that we see is that we are in the ministry itself and have very, very short contacts and short paths to our colleagues who are working in Berlin and, of course, all around the world. And I believe in a field that is as fast moving as AI, that is so important because it doesn’t really work to develop these tools in sort of a traditional IT way of doing things, right?

We used to have IT development projects that take two years, have huge teams, cost a lot of money, but that are just not fast enough to deliver on an AI solution that our colleagues, our colleagues are experiencing themselves in their private lives, right? And some of them even… some official aspects. So what we think is the big advantage that we have and what we kind of from our experience would always advertise for is kind of this fast co -creation from within an organization. And I think that is for a topic like diplomacy that is the best way of leveraging AI. And I’m happy to go into more detail about that.

Thank you. And I will later ask you more on concrete use cases. But first, I would like to switch to the geopolitical dimension. So Sharini, switching over to you. Taking a step back, AI is now in the political landscape more or less present everywhere. From the Arctis, but also here at the Summit. Can you give us a broader picture? How is AI shaping diplomacy or foreign policy in general? What is the debate and where are we at the moment?

Thank you. Thank you for the question and also the invitation to be here, which is actually also me being in my home country. So you’ve invited me to my home country, which is an interesting space to be in. But at the broader sort of geopolitical level, AI is shaping not only sort of how we use technology in our strategic communication as countries as well, but as a tool of technology diplomacy. And I don’t necessarily think this is particularly new. Throughout the history of international relations and foreign policy, technology has always shaped our foreign policy. So this is the AI revolution. But if we take it back to the Industrial Revolution, if we take it back to the nuclear revolution, if we take it back to the space race, technology has always informed diplomacy.

And today it is artificial intelligence. So the technology is not new. Yes. But the tactics aren’t. And today we are here at the AI Summit, and this is also India’s way of communicating that it is being a part of a particular technological revolution, which in its previous histories, because of colonial encounters and things, we’ve been excluded. So in this space, this is a way in which countries from our parts of the world are also trying to kind of claim a space in global technology diplomacy. And this is through AI. And what I would also kind of want to just qualify is what we’re seeing in this particular sort of AI race is narratives of competition.

So if you look at sort of policy documents coming out of the United States, coming out of China, there’s a clear connection between kind of winning an AI race or securing leadership in artificial intelligence. And if you are a country of that size and you are the country that has, invented the frontier technology and you’ve been sort of the first movers in that. if a kind of geopolitical leverage which countries like Germany and India perhaps don’t have because we aren’t at that frontier capability but that being said we’re not powerless we just have a different form of power expressing power and that is when the entire middle power conversation comes into play both India and Germany can see themselves are in fact arguably middle powers and they have different ways of using their specific leverage on an AI value chain as geopolitical leverage so for Germany historically this has been through rules and through regulation and regulatory power for India now it is making a case for applications so India and we’ve seen the fact that the summit has changed from the AI action summit which was the French presidency now is seeing India framing it as the impact summit the slogans of the summit are very very much to do with aspirations to deployment or aspirations to impact.

So that is really a way in which a middle power like India is also trying to kind of claim its position on the stack. So what you’re seeing are the great powers who are competing at the frontier level, and then there are middle powers who are claiming their specific power on the value chain in different ways. And I’ll stop there for a second.

Thank you very much. And I would like to pick up this statement that you said. But the tech is new, but the tactics aren’t. So I have here a diplomat sitting next to me. Would you agree with the statement? And how do you govern AI in the Ministry of Foreign Affairs? And would you say, is this still the right approach to AI?

Oh, well, the short answer would be no. But the topic is so broad that obviously I could give you a four -hour talk about it. But as a diplomat, as you said, one has to start by saying that the AI Impact Summit here in Delhi, where we are all gathered, showcases the broad variety of AI and the broad picture that AI is now part of every day’s life, of all strands of life. That it is a tool in communication. It is a tool in agriculture, in industrial entrepreneurship, in finance, and also in diplomacy and foreign policy. So I find that very interesting what you alluded to, that we have these revolutions all the time. like the Industrial Revolution, like the nuclear revolution after the Second World War?

And where do the foreign ministries, where do foreign policy comes in? I mean, the technological revolution created frontrunners like the UK, maybe a little bit like France. But there was a point in time when people saw that only being at the front and adapting the frontier models is not the way to success. But we have to find a way to regulate things because otherwise people will lose their lives. It’s not work safe. It’s polluting the environment. Even back then, there was a problem. Nuclear power, the same thing. There was a race in the 50s. And the Cuba crisis beginning at the 60s showed to the world that the nuclear race could not go on like it was.

But we need international cooperation to mitigate somehow the risks of it. And I think AI is at a similar point. Maybe it needs a couple of more years when the U .S. and China will actively come together and work out what limitations and regulations we have to put on the technology because the risks in the end are outweighing possible and potential benefits. And the other great question is where do the middle powers come in? And this is what India and Germany are talking about. Well, we had the speech of Mark Carney, the Canadian Prime Minister in Davos, where he actively called for the middle power cooperation. And he said, well, we don’t have the power to do that.

I think India is at a one. wonderful place because you are a digital powerhouse and you have all the structures and all the workforce to also become an AI powerhouse. I would also make the case that Germany has also some advantages. We have infrastructure, we have the money to invest into AI, and we also have industrial data to be a frontrunner. Even if we didn’t succeed at the stage of large language models, maybe when it comes to robotics and embodied AI, Germany will still have a role to play. And obviously we at the Foreign Office are there to accompany the development of this and to prepare. Prepare the ground for international cooperation. And I believe it at that because others…

thank you thank you i would like to turn now to the printing perspective um the pranav institute works at the intersection of emerging technology public policy and society from an india first perspective um how do you see um how do you see potential room for cooperation between india and germany like we hear now the middle power those are middle power i hear a lot at the summit that india is leading in a ai adoption um i wouldn’t say so maybe in germany maybe my german colleagues would agree or disagree with me but from your perspective where do you see cooperation like potential cooperation could you also go a step back and um explain to the audience where you see india at the moment maybe also in light of the ai summit

yeah can you hear me i think that’s a very challenging question to answer. Where is India at? India is at a very interesting place, certainly. India is not lagging behind. India is not yet at a place where we can build frontier models. I think the infrastructure capacity for that is very high. I do see some interesting innovation coming out of India. When we saw those 14 models that was released over 14 days and very, very interesting in the sense that this is innovation which is grounded, contextual. It is coming from the grassroots. You are able to find native language use cases. You are able to do inference at scale at much an order of magnitude cheaper costs.

So, you are seeing technical innovation which is more context appropriate coming from India. There is, of course, a large workforce which is talented in technology and there is an upscaling possibility that certainly exists when into AI and that is a very large pipeline. So I think India is a very interesting place. India is adopting, India is innovating, India is building applications and use cases, which is a very useful way to think about the technology in its early stages, right? Because there is a huge possibility of investment booms and busts that can come in when you go in a technologically challenging direction without being adaptive. So I think the focus on saying what can we solve is a very useful way to think.

I think the counselor did allude to industrial AI. That’s a fantastic use case of cooperation where you and India could possibly, Indo -German cooperation would certainly work out in that sense because there is industrial expertise, there is automation expertise in Germany, there is industrial data. India has the capability to build technology, build models. So I think if we were to identify and not worry about the race for frontier models, because transformers are not going to be the same. They’re not going to be the only technology paradigm out there and not play the game that leading powers are, but to really think as middle powers do as Sharon said and say that can we focus on sectoral expertise?

For example, AI in healthcare is a fantastic opportunity for. Indo -German cooperation, there is fantastic data available. India performs 10 times the number of surgeries that other countries do. So there’s very interesting data available. Germany has the capacity to invest. Can we cooperate? Germany has expertise in automation. India has, you know, people who can build AI models. Can we cooperate? So I think there is possibility for bilateral cooperation that, you know, gives an argument that is more than one plus one in the case of some of these. And I don’t think it’s a zero -sum game that U .S. is winning or China is winning and they’re all left behind. I think the focus on applications is really where a differentiator is possible, and that need not come at frontier -level costs.

Thank you. And I would like to focus now on this application side because this is maybe the way to react to big tech or like us as a country being in the middle between these mentioned countries. Rafa, can I hand over to you to share a little bit how you have the foreign office approach? I know that you are working on it. It’s a negotiation tool. And to what extent can open source also be a solution or might be a solution? to the situation where we are at the moment.

Sure. Yeah, so I think it’s exactly as you said, that the focus is on application. We have made a consequential, but I think important decision at the beginning that when we are implementing AI, we are focusing for most of what we do on open source technologies, not just the models themselves, but also a lot of the kind of scaffolding and applications around it. So on the one hand, for example, we are reusing applications that, for example, come from one of our state governments who have done like kind of a general chat and knowledge -based application that we are reusing. But of course, we have specific applications in the foreign office like supporting negotiations. A lot of what diplomats nowadays do is not necessarily sitting in rooms and negotiating face -to -face, but actually digging through huge piles of documents and…

trying to understand the positions of other countries, the impact that NGOs, academia, corporations bring into huge negotiation processes. And, of course, that’s, as we probably all know, is a great chance for artificial intelligence to leverage. I think one important point when we’re talking about AI and open source AI in governments is that we have seen a big trend shift or a shift in the trend last year where we have seen that a lot of the kind of leading open source AI models and actually also the ones that have been adopted in many parts of the world are coming from China nowadays. I think that’s an interesting intersection between my position as a technical observer here where we are looking at the numbers and seeing that really, like, you know, the world is adopting Chinese AI models at the moment.

And, of course, the consequences that that might bring for a country like Germany or a country like Europe. Like India on a global scale, if maybe… some of our partners are implementing Chinese AI models. So that is something that when it comes to open source, I think it’s really important that countries like India, and I think India is at a great position, and I’m super excited to see these new Indian AI models as well, these Indian LLMs, to see if there can be pushes that offer alternatives to these Chinese models.

Thank you. I would like to come back to this impact aspect. Now we heard impact in the public sector, but maybe also reflecting on the summit, AI Impact Summit. What are your thoughts on how will we now continue the conversation regarding impact, regarding really being concrete and not only writing governance formats or governance frameworks, so how can we make this cooperation very concrete and also continue where we are and face this geopolitical challenge

Shoni, yeah I like that all the geopolitical questions then somehow come back to me but I don’t blame you because my background is in international relations so that serves very well this purpose but I want to kind of also connect your point to what you just said about open source and the China connection I think we’re reaching a stage in international relations in which geopolitics and technology can’t be separated when we are integrating artificial intelligence into our daily life and into our government systems we can’t really separate the security risks that come with it And I think every country has a unique security situation. For Germany, obviously, there is the concern with Ukraine. With India, we have border security challenges as well.

We have territorial disputes that are very significant and have very serious national security implications. So the kind of technology we deploy into our systems, and if it’s open source Chinese models or any other form where we perceive or any country would perceive a national security risk, that needs to be factored in. And that is why even in our technology decisions, they have to factor geopolitical risk, which back in the day was not something that, say, companies would have to do. But now every single company that I see has now a position for a geopolitical risk advisor. And that really comes from the fact that we are living in a world in which if we are using technologies so seriously in our lives, lives, we do need to factor in how those technologies can be weaponized in a particular geopolitical situation.

And then that kind of brings me back to also some of the points that were on, you said, you know, where we, you, foreign office would like find it helpful for reports to be kind of processed to AI. As a think tank, I think I’m a little bit hurt, I have to say, because a lot of our work is producing a lot of those reports, but we will force you to read them. We’re very persistent at the German Marshall Fund. We will reach out and invite you and make you read them. But jokes aside, it is really, we’re aware also that our ability to consume information as well is kind of becoming shorter, but the world is getting more complex.

And therefore, we are also kind of preparing, even in the think tanking that we do, even in the way in which we kind of do our daily jobs, to factor in that. There will be an AI in this system, and we kind of need to put that into consideration as well.

and since there will be an AI in the system we have to make sure that we can trust this AI and that it’s also inclusive and that it’s yeah ethical in a sense or trustworthy regarding to standards so how do you and the government react to this could you also share more about the global digital compact and what is this panel about this scientific panel I think it’s called and how do we make sure that from this governance it goes to the system to the AI system like how do we make sure that the systems are aligned with our values

well that’s big question the best way to align the systems with our values is to develop to develop them ourselves right and not just procure them from from outside and I couldn’t agree more with the point that you made about the Chinese models, that even if it is open source, even if it runs on our servers, there are still Chinese models. They still have the Chinese ways and the Chinese ways of thinking, which comes through maybe not all the time. So using AI to do diplomatic work will not be the way because then every report will be the same, right? So I hope that Germany will not go the way to write the diplomatic reports now only using AI or summarizing it.

But we need our diplomats to insert that innovative thinking. And innovative thinking does not come from AI. Because AI… AI is much rather replicating, summarizing, in my understanding. The new ideas still come from the human side. As far as I make it out. Global digital compact. Thanks for the question. The Foreign Office was the lead in Germany to negotiate the global digital compact. And obviously you can make a point that this is a UN compact and the UN system is under immense pressure at the moment. So what does it achieve? And I would make the point that despite all that, it has at least produced two valuable avenues for future cooperation and discussion, two platforms.

The first is the AI panel. I think it’s called Independent Scientific International Panel on AI, but I could be wrong with the two I’s. It’s rather complicated. But it was just yesterday that the UN Secretary General made the point that the AI panel and the second one, the dialogue I will come to in a second, are the two major things where the UN is coming into the picture. And the panel has the task to put our discussions that we have on a global level about AI on a scientific basis. So those are experts, and I’m happy that there are two experts from Germany on the panel. Only the U .S. and China have also two experts.

I’m terribly sorry. I don’t know how many Indian experts are on the panel. But we’ll find. We’ll find that out. True. So they will produce a first report, a summary of where the AI science is now standing in time for the first global dialogue on AI governance, which will happen in July in Geneva in the margins or back to back with the AI for Good Summit at the International Telecommunications Union. And this dialogue serves the other big purpose of the Global Digital Compact, which is to make the AI discussion inclusive. And so it’s also the UN Secretary General nonetheless that said that AI cannot be a discussion among the few, the ones that are the front runners like the US and China.

They should not be the one to they should not. Not be the only one to set the rules, but it has to be a truly inclusive discussion about the AI. Up until now, more than 100 countries were not part of this discussion because they were not members of the European Union, not members of the Council of Europe, not members of the G7 or the G20. But they are the ones that will use AI, that will adapt AI, and they will also feel the bad results if AI is not doing what it is supposed to do. So it’s good that they have a voice at the table, that all UN member states will in July come together and talk about AI on the scientific base that the panel has provided.

So that is something that the Global Literature Compact is doing. And, of course, we can talk about geopolitics all the time, but I think that’s a way forward. And it’s to make a point. And I stop here and make a point.

Thank you. Thank you. And, Jian, let me turn to you now. And the fact that there is not a zero -sum game in a lot of this. I think the idea that we can work together to bring a larger voice beyond the worries of two countries or three countries which are able to compete at the top. I think that’s something that they shared. And I think the role of middle powers in bringing a more inclusive conversation is really important. And I think Indo -German cooperation is an opportunity for that. Including, for example, industrial AI that the counsellor mentioned or other opportunities where we can practically create tools like what Rafael is also talking about. Where we can practically create tools that are beneficial and maybe open source.

Why should open source models only come from strategically challenging sources? There could be Indo -German open source models, smaller models, not frontier models that could be beneficial.

Yeah, I can maybe react to that directly because I think it’s super critical. I don’t think some people believe or make us believe that the AI race is already over or kind of only being decided between the US and China. I don’t believe that. I think we’re more at the start of what’s going to come. And I think we can feel this at the summit. And Gunda, you asked the question, what comes next? I think next comes building and implementing AI in all these kind of fields that we have. I think we see so many ideas around here and first steps towards that. But we don’t really see widespread AI adoption in every field, in every kind of part of life.

I do think this is going to happen over the next five years. And I don’t believe for a second that this is only going to be done by the U .S. or China. And, yeah, I think that when it comes to middle powers, Germany, India, I think we are going to see much closer collaboration in like smaller groups that don’t try to kind of, you know, build dependence, right, making you dependent on us, making us dependent on you, but rather ensure that every country can bring to the table what they are particularly good at and make the results kind of improve the application of AI for everybody involved. I do think there is a strategy for that.

And I think, yeah, the way forward you have asked is to start with it and to build AI together. I think this is a great, you know, a great rally cry for

Yeah, yeah, please.

Rafal, you led me on to a very interesting trail, so I had to intervene. I think one of the interesting moments, if you think about technology again, in the 1990s was the open source revolution, right? And when you really saw operating systems, consider the frontier technology of that time being built by volunteers at a fraction of the cost. diffuse the race in a certain way or diffuse the dominance in a certain way, but also enabled accessibility across the world. So I think even coming together as middle powers, the power of open source and democratizing and reducing the factor costs of access to AI, it becomes very powerful if you draw from it. And now you led me on to a trail as well.

I just want to kind of contextualize the sovereignty thing as well.

And I do think that when we talk about artificial intelligence, it’s not just one application that we see when we use our phones or interact with a particular model, right? It’s an entire stack. And the question of sovereignty or the concerns vis -a -vis the sovereignty debate is also born out of geopolitics, right? So we don’t want to be as a particular country in which suddenly one day we wake up and our technology is not available to us because, because of something else that happened in another country. corner of the world. So the sovereignty debate is coming out of geopolitics as well. That being said, we don’t need to be beholden to it. I fully agree.

And I really like the point on us understanding what our strengths are. I mean, Germany had a high -tech strategy that came out last year. There’s also an emphasis on Germany being a space for data as a data hub. And India is trying to do that as well. Germany already has that. One of the things China is really good at actually is industrial data, because they have been collecting this data for a very long time because they automated quicker than a lot of us. And that’s something where we can collectively build competitiveness. So I do think we need to reset some of the inequalities in the AI stack and that sovereignty, as much as I kind of understand where that comes from, I don’t always think that that’s the…

best language to talk about where we are at. I do think we need more sophisticated and nuanced ways of kind of talking about a managed interdependence where I have a certain value on an AI stack. That is my strength. And the likelihood of you weaponizing that makes it very limited. So that’s why I have leverage. And I do think leveraging a country’s strength on a specific AI stack is a prominent and powerful middle power strategy.

Thank you. These are all beautiful closing remarks, but I would like to open the floor to the audience. Are there any questions? Yes, everyone around.

Hi, I’m Sreeni. I’m a student at Ashoka University. I have a question for everyone in the panel. Feel free to. answer. The question is, what are some parts of foreign policy research, decision making and implementation which can be automated by AI or that will use a significant uses of AI to sort of do your day -to -day tasks?

Maybe I can quickly answer this question. Well, I certainly don’t think that AI will make any decision in any time soon. So there’s always going to be the human that is making the decision. And it’s not going to be me. It’s not going to be my boss. It’s going to be a collaborative decision by the government and the legislature and all of that. But our job will also not go away. We will use AI to make our job easier to consume data, to consume, I would like to say information, but it’s nothing. Consuming information easier, quicker and which in turn will free diplomats to do the other time, which is connecting, which is connecting the dots, which is thinking out innovative ways of cooperation, which is, it’s basically like drinking coffee and shaking hands.

These start traveling to India and learn a lot about the situation here. So, AI will free us from tedious tasks of skipping through these very valuable documents written by not only NGOs, but also governments, and will make our lives easier, but our work will not go away. Thank you.

Okay, one more question. The lady maybe in the back.

Hi, I’m Sanjeevni, and I work in radio journalism in the UK. And my question was for you, Norman. So, specifically, So, Norman and Sharini, both of you, actually. So, you guys are doing your Masters in Journalism. I was studying about how journalists were framing the Russia -Ukraine war. And we were observing how the narratives were changing based on different outlets. But something when we were talking about how AI is coming into play, do you think AI will help change narratives for the better? And I’m not speaking from journalism point of view. In general, geopolitically, do you think the narratives will be framed in a way that’s unbiased? Or how do you think it will help in that?

I don’t take a stab. I’d be very curious to point a question, actually, to you, because one of the things I’ve been really intrigued in in my line of work is how AI has been being deployed in the media and newsrooms. And I’d be very interested to have a chat after to learn how you’re doing that in terms of methodology. But to your question on AI shaping, I think it’s a great question. narratives, I would not let AI shape narratives. I would hope we shape narratives as human beings, depending on sort of what we think and feel and analyze through empirical evidence about the world. And I would be very worried in a world in which AI, we allow AI the space to shape narratives, especially on geopolitics, because then that would depend upon what that particular AI model that is doing the narrative shaping has been trained on.

But that being said, I would also see how AI can do the harm in terms of amplifying incorrect narratives or geopolitically challenging narratives. And that’s when we know that AI cannot replace society and it cannot do. So I do think that we are in a world in which if AI, we allow AI to shape narratives, that’s not a world we want to live in. Thank you. but at the same time, if it is a world in which that can happen, we need to find the right mitigation strategies to do that. One thing that I know India is doing, Shyam, we talked about it earlier, which is these bias detection technologies that are critical. AI is a technology that, on one hand, we do need strong regulation to make sure that we can prevent the harms from doing what they can, but at the same time, we need also technological tools to deal with some of those harms and push a democratic innovation as well in AI for exactly these harms.

And I’ll stop there.

Just one sentence. If you let AI write the newspapers, they are becoming incredibly dull because it’s going to be repetitive all the time. But I agree with your point about bias. This is something that we all have to challenge and to face. And AI is helping us. That’s a good thing. AI is not only a risk, it’s also the opportunity, helping us detect bias and then contravene it. Thank you.

Just one sentence I want to add because I found your point so important. I don’t see any risk that AI is going to shape the narratives itself somehow, but of course it’s an incredible tool for actors trying to shape narratives. And we have seen this on so many fronts already. We have colleagues in the foreign office who are actually monitoring this and seeing that it’s, for example, used to amplify certain messages across social media, increasingly now across faked websites that, with the help of AI, you can pull up in seconds and suddenly you don’t have one of them or two of them, but you have thousands of them. And that is something that AI has already used quite heavily as a tool of certain actors who are trying to influence geopolitical discussions in exactly that way.

Sorry, we are running out of time, but I’m sure that the speakers stay here a little bit. So thank you for listening to our panel discussion. Maybe a big applause. Thank you.