Democratizing AI Building Trustworthy Systems for Everyone

20 Feb 2026 12:00h - 13:00h

Session at a glance

Summary

This panel discussion focused on democratizing AI and building trustworthy artificial intelligence capabilities globally, with particular emphasis on bridging the gap between the Global North and Global South. The conversation took place at a major AI summit in India and featured experts from academia, industry, and philanthropic organizations.

Dr. Saurabh Garg highlighted the challenges of international coordination in AI development, emphasizing that countries don’t need to control every layer of AI resources but should focus on governance frameworks and developing institutional capabilities. He stressed the importance of managing the interdependence of AI ecosystems spanning hardware, software, and ethical protocols.

Microsoft’s Chief Responsible AI Officer Natasha Crampton announced the company’s $50 billion commitment to bring AI to the Global South by 2030, outlining five key pillars: infrastructure development, skills training, multilingual AI development, local innovation support, and data sharing for policy making. She emphasized that AI diffusion in the Global North is currently double that of the Global South, creating an urgent need for targeted interventions.

Peter Mattson from ML Commons stressed that reliability, not capability, is the primary barrier to AI adoption today. He advocated for industrial-scale benchmarking systems that can measure AI performance across different languages, cultures, and use cases, noting that trustworthy AI requires dependable evaluation frameworks.

Dame Wendy Hall called for developing a “science of AI metrology” to measure the effects of AI systems on society, comparing it to weather forecasting in complexity. She emphasized the need for inclusive participation, particularly from women and underrepresented groups, in AI governance decisions.

The panelists agreed that trustworthy AI diffusion requires active coordination, measurement across multiple dimensions, and collaborative partnerships between governments, private sector, and international organizations to ensure equitable global access.

Keypoints

Major Discussion Points:

– International Collaboration and Governance Challenges: Dr. Garg highlighted the complexities of coordinating international AI efforts, emphasizing that the biggest challenges lie in managing the interdependence of the AI ecosystem (hardware, software, protocols, ethics) and developing governance frameworks for sharing mechanisms while respecting each country’s priorities and values.

– Microsoft’s $50 Billion Global South Initiative: Natasha Crampton outlined Microsoft’s comprehensive five-pillar approach to democratizing AI in the Global South: infrastructure development (data centers, connectivity), skills training (including 2 million Indian teachers), multilingual/multicultural AI development, local innovation support, and data sharing for policy making.

– Trustworthy AI Through Reliable Benchmarking: Peter Mattson emphasized that AI adoption is limited not by capability but by reliability concerns. He discussed ML Commons’ work on developing industrial-scale benchmarking systems for safety, security, and cultural sensitivity, stressing the need for common measurement standards to build trust in AI systems.

– Inclusive AI Development and Gender Representation: Dame Wendy Hall passionately argued that AI development is “totally male dominated” and stressed the critical need for women’s involvement in AI decision-making processes, particularly around safety issues. She advocated for developing a “science of AI metrology” to measure the societal effects of AI systems.

– Practical Implementation Challenges in Developing Countries: The Gates Foundation representative (Harish) discussed real-world challenges including connectivity issues, energy consumption, the need for edge computing solutions, language barriers, and ensuring AI reaches the “bottom 50% of the pyramid” rather than creating further digital divides.

Overall Purpose:

The discussion aimed to explore how to democratize AI access globally while ensuring trustworthiness, with particular focus on bridging the gap between the Global North and Global South through collaborative efforts, proper governance, reliable measurement systems, and inclusive development practices.

Overall Tone:

The discussion maintained a constructive and collaborative tone throughout, with participants building on each other’s points rather than debating. There was a sense of urgency about addressing AI inequality, but also optimism about potential solutions. Dame Wendy Hall’s contributions added some passionate advocacy and occasional humor, while other speakers maintained a more measured, technical approach. The tone remained professional and solution-oriented, with all participants emphasizing the importance of partnership and measurement in achieving trustworthy AI democratization.

Speakers

Speakers from the provided list:

– Justin Carsten – Moderator/Host of the panel discussion

– Dr. Saurabh Garg – Working on international collaboration efforts in AI, has vast experience in coordinating international efforts

– Natasha Crampton – Microsoft’s first Chief Responsible AI Officer, leads the Office of Responsible AI at Microsoft, works on putting Microsoft’s AI principles into practice and collaborates on shaping AI laws, norms, and standards

– Peter Mattson – President of ML Commons, CEO, Senior Staff Engineer at Google, founded ML Commons, previously head of programming systems and applications group at NVIDIA, works on AI benchmarking and reliability

– Wendy Hall – Dame Professor, Regius Professor of Computer Science and Associate Vice President International Engagement at University of Southampton, Director of Web Science, Dame Commander since 2009, Fellow of the Royal Society and Royal Academy of Engineering and ACM, co-chair of UK government’s AI review, member of AI council

– Participant – Works with the Gates Foundation in India, focuses on strategic partnerships between Indian researchers and global partners in areas including vaccine preventable diseases, disease surveillance and modeling, works in health and agriculture sectors

Additional speakers:

None identified – all speakers in the transcript correspond to those in the provided speakers names list.

Full session report

This panel discussion at an AI summit in India brought together experts from academia, industry, and philanthropic organisations to address the challenge of democratising AI access globally whilst ensuring trustworthiness, particularly in bridging the gap between the Global North and Global South.

The session was moderated by Justin Carsten, who opened by noting the significance of the summit and introducing each panelist. The discussion featured Dr. Saurabh Garg, Natasha Crampton (Microsoft’s Chief Responsible AI Officer), Peter Mattson from ML Commons, Professor Dame Wendy Hall (who corrected that her title is “Professor Dame, not Dame Professor”), and a representative from the Gates Foundation.

Reframing AI Democratisation as a Governance Challenge

Dr. Saurabh Garg opened the substantive discussion by reframing AI democratisation from a technical problem to a governance issue. He emphasised that “a bigger challenge might be to manage the interdependence of the AI ecosystem because it spans hardware, software, and the protocols, so to say, or the ethics around that.”

Garg identified two critical barriers: establishing sharing mechanisms and protocols, and developing talent and institutional capabilities. His key insight was that “infrastructure can be acquired, but expertise has to be developed,” highlighting that democratisation requires more than just sharing computational resources.

Microsoft’s $50 Billion Global South Initiative

Natasha Crampton announced Microsoft’s $50 billion commitment to bring AI to the Global South by the end of the decade, addressing the disparity where AI adoption in the Global North is roughly double that of the Global South.

Her five-pillar approach includes infrastructure investments with sovereignty controls. As Crampton explained: “we design our data centres and also the services that run on top of them with a recognition that there needs to be real agency for the countries hosting those data centres.”

Crampton provided crucial historical context: “What we’ve learnt from the history of diffusion of other general purpose technologies like electricity, for example, is that the countries that succeed in these really transformative economic moments are not actually the countries that necessarily invent the new general purpose technology. They’re the countries that diffuse and adopt that technology fastest.”

The initiative includes training 2 million Indian teachers in AI skills, recognising that “when you teach teachers, you’re teaching students, and therefore the workforce of the future as well.”

A key component addresses multilingual and multicultural AI development, as “AI is no good to you if it does not work in the language that you speak and the culture in which you use the system.” Microsoft is collaborating with ML Commons to expand safety benchmarks to include Hindi, Tamil, Malay, Japanese, and Korean languages.

Trust and Reliability as Primary Barriers

Peter Mattson challenged conventional thinking about AI limitations: “If I had to point to anything that’s holding back AI today, it’s not capability, it’s reliability. Is it correct? Is it secure? Is it safe all the time?”

His argument centred on trust: “people are smart, we don’t adopt things we don’t trust.” People won’t provide sensitive information to AI systems they don’t trust, regardless of technical sophistication.

Mattson emphasised the need for “industrial quality benchmarking, which is what we need for industrial level reliability,” moving beyond experimental research to dependable frameworks for real-world applications.

ML Commons’ federated evaluation approach enables “healthcare benchmarking for reliability, for correctness, against very, very diverse data sets, potentially around the world” whilst respecting data sovereignty requirements.

Cultural Sensitivity and Inclusion Challenges

Dame Wendy Hall brought a critical perspective, noting that AI development is “totally male dominated” and that “50% of us are women and we’re not involved in the discussions about keeping us safe.”

She highlighted regional differences in AI perceptions: whilst Western countries focus on deepfakes and safety concerns, “here the kids are going wow what an opportunity right… they’re not worried about the deep fakes yet what they want is to get the information to their people in the fields the farmers in the fields in rural India.”

Hall called for developing “a science of AI metrology,” drawing parallels to weather forecasting: “if we can do that we can do flipping AI because that’s complicated the thing about AI is of course it’s got people in it not just physical objects doing things systems so it’s harder in that sense.”

She also criticised the conference format itself, noting “250,000 people here but you end up talking to rooms of tens of people,” and mentioned her “love hate relationship” with such events.

Practical Implementation in Development Contexts

The Gates Foundation representative focused on ground-level challenges, particularly around sustainability and accessibility. They questioned whether AI democratisation requires “large centralized models” or “dispersed decentralized models on the edge,” especially for countries with poor connectivity and limited energy infrastructure.

Using pregnancy risk stratification as an example, they noted: “the rules in Uttar Pradesh, for example, may be different from the rules in Telangana. How do you make sure that if you have a tool that supports frontline workers in understanding and improving identification of risk of pregnant mothers, how do you make sure that it works in that context?”

They emphasised economic constraints: “many governments in the global south may not be able to afford the large amounts of money that may be needed for a long period of time,” and warned about creating divides “not just between global north and global south, but even within countries.”

Measurement and Collaborative Frameworks

A consensus emerged around the importance of measurement for building trust. Mattson advocated for “common yardsticks that you use to measure progress,” whilst Hall’s proposed metrology science would incorporate interdisciplinary approaches to measuring AI’s societal impacts.

The discussion touched on sovereignty concerns, with Microsoft’s approach of building sovereignty controls into data centres whilst maintaining global connectivity representing one model for balancing global scale with local agency.

However, challenges remain unresolved. As Crampton noted: “sometimes there are direct conflicts between what one jurisdiction wants and what another jurisdiction has sort of declared as a matter of law.”

Conclusion

The discussion revealed that democratising trustworthy AI requires addressing interconnected technical, governance, cultural, and social challenges simultaneously. The consensus around measurement and evaluation provides a potential pathway forward, but multiple methodologies will be necessary.

The historical perspective on technology diffusion offers both hope and urgency: success requires deliberate action across multiple dimensions. However, as Hall’s critique highlighted, true democratisation requires fundamental changes in who participates in AI governance decisions and how success is measured, ensuring that those most likely to be excluded from AI benefits are centred in both design and governance of these systems.

Session transcript

Justin Carsten

Thank you. Thank you. you you Thank you. Thank you. Thank you. Thank you. you you you you Thank you. Thank you. Thank you so much, Dr. Garg. It really highlights one of the things about collaboration, and I’ll be talking to… a number of the panelists about… about that and that i’ve been so impressed this week at how much people are really coming together for the community you know this is a much bigger summit than we’ve had previously many more people really opening it up to everyone but if i can just ask you one thing on because the working group that you’re doing i think is is excellent it’s going to be really important um what do you see is the biggest challenge around that what do you think you know your vast experience that you’ve got of coming together do you think um there’s any particular challenges in coordinating that international effort

Dr. Saurabh Garg

of course see there would be a number of challenges but i think as i mentioned that one doesn’t need to really control every layer of the resources that is there and while foundational resources the foundational computer resources sharing would be a major challenge but i think a bigger challenge might be to manage the interdependence of the AI ecosystem because it spans hardware, software, and the protocols, so to say, or the ethics around that. So I think one of the biggest challenges would be the governance around this sharing mechanisms, sharing protocols, and managing the framework. And the other would be what would be the talent and the institutional capability, which is in a way required. Well, the infrastructure can be acquired, but expertise has to be developed.

And I think that’s critical to ensure that if you want to democratize and ensure that GlobalSoft is integral to that, and that’s where it would be. And I think, you know, we don’t need to focus so much on whether each country is owning each layer of the AI, but how one can do that. What is the capability and confidence in the systems that manage that we have the required methods to ensure that it takes care of the priorities and the values that each country wants to push forward?

Justin Carsten

Thank you so much. And I agree with you. It’s a big challenge, but I’m glad that you’re there to take that forward. And this week, you may have seen the photograph of Modi here with many of the leaders in tech. And it’s a great pleasure that one of the large organizations in the private sector, Microsoft, has got representation here. So I come to you, Natasha. So Natasha Crampton is Microsoft’s first chief responsible AI officer and leads the Office of Responsible AI. And it was interesting how long that’s been going. I heard earlier this week. But she’s putting Microsoft’s AI principles into practice by defining, enabling, and governing the company’s approach. to responsible AI. The office also collaborates with internal and external stakeholders to shape new laws, norms, and standards to help ensure that the promise of AI technologies is realized for the benefit of all.

As I said, that’s been a key theme. So I saw Brad speak yesterday. It was a fantastic speech, and that was based upon a recent blog post that you and Brad put out just a couple of days ago. So can you tell us a little bit about that for some people who haven’t had the chance to absorb in this session, please?

Natasha Crampton

Sure. Thank you, Justin, and it’s a pleasure to be here with the panel and the audience today. So I think our announcement earlier in the week was about how Microsoft is contributing to bringing AI to the global south, and the headline that you might have seen is that we’re on. Hi. to spend 50 billion US dollars in order to do that by the end of the decade. What we’re seeing from the diffusion data that we have access to and that we’ve publicly published already is that there is an urgent need to focus on the diffusion and what it’s going to take to do that broadly and beneficially of AI to the global south because we are already seeing that diffusion in the global north is roughly double what we see in the global south.

And so for Microsoft, as a private sector player here, we think we have a role to play in helping to close that gap and we see it as being centred on five different components. First, as Dr. Garb mentioned initially, we need to help build out the infrastructure that is needed for broad AI diffusion. So this is both… Investments in data centres to power AI applications, but it’s also investments in connectivity as well. There are real electricity needs that need to be met. We’re trying to do that with an eye towards the sovereignty of countries around the world. We realise that the world is a fragmented place, and so we design our data centres and also the services that run on top of them with a recognition that there needs to be real agency for the countries hosting those data centres.

And so we have a range of different controls that we put in to our data centres, which include sovereignty controls and public clouds. Sometimes we build private clouds. But most importantly, it’s all built on a foundation of collaborating with our government partners around the world. The scale of the infrastructure… The infrastructure investment that’s needed is just so great. It’s really hard to see how we’ll achieve what we need to without significant private sector investment as well as funding from a range of different sources as well, governments, venture capitalists and others. So the first limb is all about infrastructure. The second limb is all about skilling. What we’ve learnt from the history of diffusion of other general purpose technologies like electricity, for example, is that the countries that succeed in these really transformative economic moments are not actually the countries that necessarily invent the new general purpose technology.

They’re the countries that diffuse and adopt that technology fastest. And if you look back at history, skilling turns out to be one of the major unlocks to that adoption and broad diffusion. So, as I said, We’ve made a range of skilling announcements. One that I’m particularly energised by myself is a very specific one focused on educating educators to help them with an AI -driven educational future. And of course, when you teach teachers, you’re teaching students, and therefore the workforce of the future as well. So we committed to teach AI -specific skills to 2 million Indian teachers in partnership, of course, with Indian national standards and training institutions, which is an exciting thing to me to support the future.

Third, the third limb is all about investments in multilingual and multicultural AI. You know, AI is… It’s no good to you if it does not work in the language… that you speak and the culture in which you use the system. So we’ve been pleased to collaborate with Peter Mattson from ML Commons on an expansion to represent Hindi, Tamil, Malay, Japanese, Korean, of some safety benchmarks that ML Commons has played a key role in standing up. But we’re working upstream of testing and evaluation as well. So we’re pleased to announce a Lingua Africa initiative where we are working with local communities in partnership with the Gates Foundation and others to really make sure that we’re collecting lots of that really rich local data with and for communities.

All of that data is not well represented on the internet and spoken languages. And spoken languages in particular require that careful collection. is all about supporting local innovation. I think it’s critically important that as the private sector we really deeply understand that AI will only be meaningful in people’s lives if it’s actually solving the local problems that matter to them. So we announced some initiatives here in India and further afield that are designed to really support that local innovation. Last, we announced also as part of the new Delhi Frontier AI commitments that several leading Indian AI companies and Frontier AI companies from around the world signed on to yesterday that we’re going to be contributing our data as to what we can see about adoption and usage of AI in the economy into some central projects.

Including one led by the World Bank. So that policy makers are in a good… position to understand how is AI being adopted in the economy? Where are the places where it’s going faster than expected? Where are the places where it’s going slower? Because I think that kind of data is incredibly useful for policy making because it allows you to spot those places where you might need a skilling intervention or an infrastructure intervention.

Justin Carsten

That was fantastic. And if you ever want to know about really believing in something, having such a complex blog and then just reeling off the five pillars, and that really just shows that commitment, I think, that we’re seeing from Microsoft taking that leading role. And actually, collaboration has been, since Brad’s presidency really, has been one of the things that he really encouraged about saying, look, we’ve got to work together.

Natasha Crampton

Absolutely. I mean, not one of those five limbs is possible without deep partnership. And that coordination of those five pillars is really important. Thank you. Thank you. of those partnerships and deeply investing in them over time is really what’s going to give us the outsized impact here.

Justin Carsten

And if we think about this, because Microsoft is a global corporation, you’ve got lots of countries, each with, just as Dr Garg said, they’ve got their own customisations, they think. They’ve got their own local laws and regulations. And some things, you know, there’s something called the Brussels effect around GDPR, for example, which went pretty global, but it’s not the case for AI, for example. How do you think you manage that challenge of trying to make sure that it’s broad enough but focuses for the individual needs of nations? Have you come across that challenge?

Natasha Crampton

Yes, that is part of what I work on day in, day out at Microsoft, because part of my role is working very closely with our product teams to make sure that we are building our product. our models in a way that’s trusted and trustworthy by design. And so we are building products and technologies that we aim to share with the world. And it is absolutely true that not every part of the world has the same rules or expectations. And part of what we need to do is to make sure that we’re building technology in a way that has enough sort of controls and choices that people can make downstream of what we choose to do at Microsoft to apply that technology in their own context.

So we ourselves do have a point of view about how we want our technology to show up in the world. So, you know, we do think carefully about if we’re making available a service that’s got some configurable controls, we do think carefully about what we think the default should be. But we also really do recognize… the need for that agency, and we do deeply understand that not every part of the world is homogenous. I think it’s, you know, here in India, it’s just a beautiful place to recognize the sort of linguistic and cultural diversity of the world. Quite honestly, if we don’t build technology that can be easily adapted and applied in people’s local contexts with their values, with their laws, we’re just missing the opportunity to, you know, have our technology reach the world.

So there are complex challenges. Sometimes there are direct conflicts between what one jurisdiction wants and what another jurisdiction has sort of declared as a matter of law. They can be worked through, and this is partly why you also need a great partner ecosystem, right? Being able to make available models open source or in an open -weight space. which Microsoft has long done, for example, with our five family of models. This is another way of empowering the ecosystem to adapt and build based on that.

Justin Carsten

Thank you so much. And you just touched on, you mentioned ML Commons and you touched about culturally sensitive. And it’s interesting, there is a report that’s been released by ML Commons this week on robust and defensible benchmarks. And part of that was some great work from the Singaporean agency IMDA, which the response from an AI, it has to be culturally sensitive. And that’s the point that you made. I think culture is important because what is seen as acceptable in one culture may not be in another. So that brings me nicely to Dr. Peter Mattson, who is the president of ML Commons and also a CEO. He’s a senior staff engineer at Google. So he founded ML Commons himself and was previously the head of the programming systems and applications group at NVIDIA.

So on that ML Commons, I think it’s done some great work, as we’ve heard. It’s played a major role in benchmarking performance and efficiency of AI. How do you see that open benchmarks can contribute to building sovereign capabilities, Peter?

Peter Mattson

I think that’s a fantastic question. I’m going to start with a very broad context and then narrow it down to that specific. And the broad context I want to start in is why is trust and reliability so vital for AI? AI has tremendous potential to change everything we do. But in order for it to do that, people need to feel comfortable adopting it. And we’re all… smart, we don’t adopt things we don’t trust. You don’t give them your banking information. You don’t give them your business information. You don’t give them your medical information or trust what they say or do about it if they’re not reliable. And so the question becomes, how do we make AI reliable?

Because if I had to point to anything that’s holding back AI today, it’s not capability, it’s reliability, right? Is it correct? Is it secure? Is it safe all the time? And if we can make AI truly reliable, the potential for benefits to everyone around the world, and frankly, the potential for businesses and markets is fantastic. But the way that we drive that is with metrics, is with evaluations. AI is an incredibly complex black box system. So to make it better, you need to have common yardsticks that you use to measure progress. And we need those common yardsticks back. widely for all aspects of reliability. So you alluded to the work on security with IMDA. Natasha alluded to some of the work around multilingual safety that we’re collaborating with Microsoft on and with folks at Google as well.

These are examples of what’s necessary to drive that push towards reliability. But they’re very technically hard. This is something that I don’t think people appreciate enough. They see someone publish a paper. We made a benchmark for something, right? And they made a data set and they did it once. But there’s a tremendous amount of technology to go to industrial quality benchmarking, which is what we need for industrial level reliability. There’s one. We need to work to take the experiments we’re doing in multilingual benchmarking and turn those into a dependable framework that empowers people around the world to produce very high quality. quality, multilingual safety and security benchmarks, and then to maintain and evolve them over time, right?

If ML Commons can help lift the resources there so that people can make the choices about language and culture where they have expertise without having to grapple with the really hard technical questions of how you do AI benchmarking, we hope that could be very empowering. An example from the healthcare space, we have a MedPerf project that uses what we call federated evaluation, where it sends models out to different facilities and then tests them on a small bit of data and accumulates the results. This is how you do healthcare benchmarking for reliability, for correctness, against very, very diverse data sets, potentially around the world. It’s technology like that, like dependable industrial scale multilingual safety and security, or medical benchmarking, or medical benchmarking, made possible by the with data sets across disparate legal systems through technology like Federated Eval and Confidential Compute that we believe really unlocks that future of high reliability systems.

Justin Carsten

That’s excellent. Thank you. And the repeated use of that term reliable. So what we need is reliable LLMs, but we need the reliable benchmarks, as you said. Yes, yes. And I think this point about healthcare is really interesting because what we need to do is, you mentioned industrial scale as well, we need this process that can be trusted. And that’s one thing that I found working with ML Commons, how we all come together, the people from industry, many academics around the world. You just look at any of the papers released, so you can go to the website, and how many authors and how many years of expertise is donated to that effort. Yes, yes. Where do you see, Peter, the next sort of big movements for ML commons?

Because these yardsticks will change. You’ve done healthcare. Where do you think is the important area for you in benchmarking in the near future?

Peter Mattson

I think thanks to the contributions from all of those experts. I truly think it is a testament to the industry that we are getting very in -demand experts from some of the leading companies to contribute to this work. Like, people really care about doing AI right. That is unarguable if you look at, as you say, the author list. What we need to do is leverage that expertise to scale. It’s not enough to do a benchmark and publish a paper. We need to make that benchmark available to the industry. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper.

It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. It’s not enough to do a benchmark and publish a paper. prompt response. You ask a question, you look at the answer, you see whether it’s safe or secure or correct.

But the future, as everyone knows, is multi -turn and agentic. And so we need to drive, you know, wider and deeper at the same time. There is tremendous demand for what we do. It is tremendously resource -intensive, and

Justin Carsten

You mentioned the work of Google, so I’m going to come to Dr. Aya from the Gates Foundation in a moment, just talking about some of the conversations. So we were hoping to have Vint Cerf, who some of you may know. I know, Wendy, you know him very well. But he doesn’t travel so much, does he? No, yeah, that’s the thing. He couldn’t travel. He’s got some issue that he couldn’t. improve public health and economic development. He’s a strategic partner between Indian researchers, you’re based over here in India, global partners and Gates Foundation teams in areas including vaccine preventable diseases, disease surveillance and modelling. So thank you for joining us today. We’ve heard a little bit, of course, India has really pushed forward with its digital public infrastructure.

And we’ve heard in the last session, Dr. Gog was in from Sanjay Jain, your colleague, about Mosef, which is modelled on Adha in some ways and is an open source initiative. So what I’d like to ask you is, where countries lack foundational infrastructure, what role do philanthropic organisations like the Gates Foundation play in enabling access to… to trustworthy AI capabilities?

Participant

Thank you so much for inviting me. I think this is obviously a very complex question, not fully settled, I will say for sure. So I mean, most of my experience in this field is in India. So I think, first off, I’d like to start by saying it’s great that India is hosting this summit. It’s fantastic. And showcasing a lot of the work that the country has done, the capability and the use cases that we are very closely supporting. I think the trustworthy question is very much, and I would say sustainability as well is another question that we have to think about, is about what sort of models do we need to have? Are they large centralized models?

Or are they dispersed decentralized models on the edge? do we need in countries with poor connectivity so trustworthiness has got many aspects to it is it going to be ready to work when you want it to work suppose again my work a lot of it is in health and agriculture and things like that so if you are a front line worker how do you make sure that they can if they have to make inferences and primary care can they make inferences if needed on the edge if you are a health system person and you want to improve the working of a health system making sure the right experts are in the right facility the right medicines are there patients are taken care of there is a great opportunity to make this very high quality but again the question becomes how do you access the compute how quickly can inferences come how easy it is to prompt there is all this which is very, because if it doesn’t work well, then you lose trust.

That’s the, it just doesn’t work. The next level question is language. I think Dr. Garth talked about it, the whole Bhashani project in India and there are similar projects that we’ve been involved in and there’s been a lot of debate even within the foundation as to which models can perform on language well. Which systems can interpret super complex, I think we heard from the other speakers about how complex this is, what works well. So trustworthiness will partly come from how systems respond and the lived experience in terms of simple things like, is it accessible? Is it the right language? Is it relevant? I mean, India is a continent on its own between different states, the health system and approaches are often different based on local policies.

how does it work in terms of policy in a particular state? One thing I’m particularly familiar with is pregnancy risk stratification. We talk a lot about how to reduce maternal mortality, infant mortality, stillbirth. The rules in Uttar Pradesh, for example, may be different from the rules in Telangana. How do you make sure that if you have a tool that supports frontline workers in understanding and improving identification of risk of pregnant mothers, how do you make sure that it works in that context? So this context is important. I think trust has all of these things built into it. I’ll also talk a little bit about sustainability questions. Sustainability also requires these kinds of questions to be answered well.

What’s the energy consumption? Are there simpler, lower parameter, lower energy consuming models rather than the giant models? To me, it’s a core question. And I think… it’s nice to know that there are researchers in the country who are thinking about that. Beyond that, can compute hardware itself look different? You know, beyond digital, let’s say, I saw these researchers recently looking at, you know, multi -parameter, multi -state compute capabilities and that was really fascinating. I just saw it two weeks ago because I was prepping for a bunch of meetings. Can those be great opportunities? Maybe they are further in the future to improve the likelihood of edge computing and edge inferences. So there’s a lot of, and then I think finally, open source.

I think open source is going to be in my mind a critical aspect of it. You’ll have to see how far open source movement takes track here. I believe because many governments in the global south may not be able to afford the large amounts of money that may be needed for a long period of time. How do you do these use cases well? So that I think is going to be another aspect of it that allows for adoption, trust at the highest levels. Again, I’m talking about the bottom 50 % of the pyramid. Top 10 % of the pyramid, they’ll do what they have to do. But ultimately to build trust, you need to get to the bottom 50 % of the pyramid.

And so there are different in quotes, markets here at UL. People who can pay at different levels. Even within a country like India, obviously there’s multiple different levels. How can you make sure that this thing can reach everybody and don’t create a divide, not just between global north and global south, but even within countries, you want to make sure that this doesn’t create a divide. And that’s, I think, another important part of building societal trust. The last point, which I think is also important is, what is the impact on society of this technology? I think this is going to be an important one as well. Are you able to create jobs, employment, and there’s a meta question about how does

Justin Carsten

Thank you so much. And we’ll come back to some of those points in a minute if I may, Harish. Because, as you may have seen, we’ve just been joined by Dame Professor Wendy Hall, someone I’ve…

Wendy Hall

Professor Dame, but don’t mind. Carsten, you should know that. You’re a Brit.

Justin Carsten

I’m not a Dame. But if you were a Sir. It’s always Professor Sir. But if I keep being nice to you, maybe you’ll put a word in for me. So I’ve known Wendy for a long time. She’s a Regis Professor of Computer Science and Associate Vice President, International Engagement at the University of Southampton, where she’s also Director of Web Science. There are so many accolades. She’s been a Dame Commander since 2009 and is a Fellow of the Royal Society and the Royal Academy of Engineering and the ACM and was President of lots of those organisations, including the British Computer Society, BCS, sorry. and most notably she was the co -chair of the UK government’s AI review and a member of the AI council.

We’ve talked also about skills actually, Wendy. We were both on the, I think you were probably leading it, but I was just a member of it, the review with Nigel Shadbow into computer science, if you remember.

Wendy Hall

No, he did that one. That was Professor Sir Nigel. No, I didn’t.

Justin Carsten

Okay, okay. Anyway, you’ve been involved in advising many governments around the world and could you tell us a little bit about the UK’s approach to developing sovereign AI capabilities?

Wendy Hall

No, I’m not going to answer that question because this is a trustworthy panel, right? And I want to talk about trustworthiness. Okay. And that’s why I was asking what the panel was about because I’m doing three panels this morning and I’ve got a lunch date to go to, so an important one. So I was asking Peter what the panel was about and he said, because it’s about trustworthy AI, right? Yeah. so I want to say if you don’t mind Carsten I could tell you what the UK is doing it’s very parochial I’m very excited that this conference has been in India but I have a love hate relationship with it it’s been a really difficult conference to navigate 250 ,000 people here but you end up talking to rooms of tens of people ok it’s out on YouTube does AI need this sort of jamboree I don’t know for the future but it is fabulous to have the spotlight on India I’m a member of the MOSIP

Justin Carsten

of course you are

Wendy Hall

I’ve been involved I’m in awe of what India has done with the Aadhaar and built the digital public infrastructure and I want to see how that works I would love to see how that works in the UK but it doesn’t translate it works in developing countries it’s much harder to translate it to an old world that has long established rules and regs and ways of working and anyway so that’s I’m really excited it’s here and it was fabulous also to see the young people here because in the UK and I think it’s probably true in most of Europe and the US people are really worried about AI they’re scared because that’s what they get, they get scaremongering they’re scared it’s going to attack them they’re scared it’s going to wipe the world out they’re scared they’re going to lose their job here the kids are going wow what an opportunity right and for India I mean that’s been an eye opener for me I mean I know I’ve been working in India long enough to know I mean I helped introduce the web into India right web and internet and the website and stuff work I’ve done here and I know what you can do with the power of that technology for people that can’t read and write and live in the rural areas I mean it’s just amazing what it does, add AI on top of that, they’re not worried about the deep fakes yet what they want is to get the information to their people in the fields the farmers in the fields in rural India I suppose deep fakes, I mean I don’t know but that’s not what they’re worried about at the moment so it has been fabulous and I love the slogan here, in India AI is all inclusive but it isn’t AI is missing out 50 % of the population right this technology and I’ve been fighting this sort of thing all my career totally male dominated totally male dominated and I love, I’m very sorry but the way we talk about women’s safety women aren’t involved in these discussions right?

children aren’t involved in these discussions 50 % of us are women and we’re not involved in the discussions about keeping us safe actually we need to keep men safe too right? men suffer from deep fakes as much as women do so you know well maybe someone’s not agreeing with that but you know it could be disproportionately hitting women and children but I don’t want to exclude the men here so I have become I have become even more passionate I talked about it in my keynote on Wednesday not in the talk itself but in the conversation that it’s so important that this is really all inclusive and that women are involved at the top level in the decision making about what we do and I think take for example the Australian experiment to stop the kids under 16 using social media.

Now that is an experiment. Everything about this world is a global experiment and people are doing different bits of it. The web was like that. The web itself from the genius that is Tim Berners -Lee was a worldwide experiment. There are many different ways that you could have built a hypermedia network on top of the internet. Boy, I tried to do one myself. And it was better than the web. But what Tim did was give it away, make it fantastic, make it open. And actually that led to the rise of the use of it. But it’s also left us with the stuff we’ve got today. Because anyone can do anything on the web. So bad people can do bad things.

And bad things happen unintentionally. The unintended consequences is what I call my talk on Wednesday. So this ban on social media, we need to we’ve got to be able to study the effects. Now, I know the Australians are. We heard Macron say here in France it’s going to be under 15. Keir Starmer’s saying under 16. But he changed his mind on a penny, so it’ll probably change. But that’s a joke for the Brits. But I think Spain has said under 16. In the US, of course, Trump says, no, we won’t need to worry about safety. But I made this joke in the other panel. And he’s the man that drank bleach during COVID. But the point is we have to study.

And people say, oh, it’s all moving so fast. The alpha males say that, right? The alpha males say, it’s all moving so fast. And I’m bigger, better, faster, and cheaper than you are, right? All that sort of alpha male stuff. We have to think about how we actually measure the effects of what we’re doing. So… two good things that have come out of the UK this is my last point just this last month the National Physical Laboratory I’m their AI advisor but that’s beside the point it’s like the UK equivalent of NIST they do our metrology it’s a word I’ve learnt to say very well weather forecasting is metrology studying the weather if we can do that we can do flipping AI because that’s complicated the thing about AI is of course it’s got people in it not just physical objects doing things systems so it’s harder in that sense but the National Physical Laboratory announced two weeks ago backed by the UK government the Centre for AI Measurement and the UK AI Security Institute which was founded by Rishi Sunak at Bletchley Park from Bletchley Park is part of the network of security institutes.

And the US, this is the man again who drank bleach during COVID, says no regulation. So we can’t talk about the network being a network of safety institutes. Why would we want to be safe? Sorry, joke. But they’ve renamed it the Network for AI Measurement and Evaluation. Now, this is brilliant. Brilliant. So with my ACM hat on and everything else I can do in the dying embers of my career. No, it’s not dying yet. But the, is to start a science of AI that’s about AI metrology. But what we’re doing, of course, is we are measuring the effects of social machines, which is difficult. You have to like, so, you know, the social scientists have taught me how you have to gather the data.

How do you gather the evidence? and we can do it we don’t have there is time to do this the world is not going to end at the end of this year because of AI other things yes but not because of AI so that’s where I want to leave you the thought I think if we can develop this new science put all our the compute power the best brains from social science and computer science and psychology and all the other disciplines we need, the law, everything we can really start to think about how we measure trust one of the metrics in AI metrology will be the trust factor I leave it there thank you very much a round of applause please thank you and I’m ever so sorry you can ask me what I’ve got to go in two minutes

Justin Carsten

I’ll ask you one thing very briefly then open data you’ve been a proponent of right Tim and Nigel

Wendy Hall

yeah yeah yeah yeah yeah

Justin Carsten

so I just wanted openness collaboration is important we’ve talked about open source what role do you think open data has in trustworthiness

Wendy Hall

well there’s two things about that, the open data movement has been really important but not all data can be open it can’t be and I mean you can have data that is exchangeable shareable that won’t necessarily be open so another thing I’m on is the UN, it’s the CSTD Commission for Science and Technology in the UN data governance working group and I could tell you in much more detail about that for me data governance we ignore that when we talk about AI governance we ignore data governance at our peril and we’ve really got to build on that from the UN report we did the General Assembly accepted all the points we recommended they’re being implemented that’s the other panel I should have been on today there’s a UN panel and they accepted everything that we recommended the global scientific panel the global dialogue the global fund and the Secretary General yesterday asked for three billion that’s not very much you know for a global fund to develop AI in the global south but our recommendations on data governance were not accepted because people would not the countries would not vote for them because it’s so difficult it’s so complicated and so there’s another thing I’m working hard on is how can we actually get some you know how do we do cross -border data sharing how do we get the data flows so we can actually share data sets and another thing we need to do which is something I want to do is build data tell people where the data is we need data repositories or at least registries that’s around the world so researchers know where the data is so they can do this study I’ll leave you with that that’s something else I was on my agenda

Justin Carsten

thank you so much Wendy I’m going to Yes, thank you. Thanks so much. I’m going to go to each of the panelists for just 30 seconds. I’ll start with Dr. Clark, then Harish, then Natasha, and then Peter, just to make us busy. Just one comment for the audience about how we really push this democratizing AI and trustworthiness.

Dr. Saurabh Garg

Yes. I think one issue which I mentioned in the earlier panel is that we perhaps need to give a lot more attention to the models because that will also help more efficient models will help reduce the requirement for compute and energy, which is among the biggest costs presently. And having models which are more domain specific would also enable better usage of those models and widen diffusion across. Thank you so much.

Justin Carsten

Harish.

Participant

Just very quickly, I think real world evidence is going to be very important in terms of, is it actually useful? I think we all assume it’s useful, but I’m talking about social and the development sector. I can imagine so many ways it’s useful, but it would be good to make sure we build evidence on how it can be trusted and, of course, be useful, metricize this a bit more. Thank you.

Justin Carsten

Thank you. Ms. Asha? Well, I

Natasha Crampton

think one of the points that has come out clearly in this discussion is that trustworthy AI diffusion is not going to just happen by itself. We have to make choices that lead to that outcome. And so for that reason, I am excited about these attempts at measurement in multiple dimensions, measurement of the systems, but also measurement in the changes of our economy so that we can then start to see whether the interventions that we’re putting in place are actually having the desired effect. Because we get to write this future, but we have to actively guide it. And I think data in multiple dimensions is really important. keys are there. Thank you. And the

Justin Carsten

final word on measurement should go to Peter. So Peter. I’m going

Peter Mattson

to echo the obvious point, which is that measurement is tremendously important. And then the hidden point, which is the scope of measurement is vast. And so we need to get really good at it, both in terms of quality and the efficiency, the cost efficiency with which we can implement it and with which we can evolve it. Thank you. Could you

Justin Carsten

please give a round of applause to an excellent panel. Thank you so much. Thank you. Hello, hello, hello, hello, hello. Hello. Hello. Thank you. Thank you.

Dr. Saurabh Garg

Speech speed

132 words per minute

Speech length

297 words

Speech time

134 seconds

Governance of AI resource sharing and talent

Explanation

Dr. Garg highlights that the biggest challenge is governing the sharing mechanisms, protocols and the talent needed to manage the interdependent AI ecosystem that spans hardware, software and ethics. Effective governance is required to ensure that each country’s priorities and values are respected.

Evidence

“of course see there would be a number of challenges but i think as i mentioned that one doesn’t need to really control every layer of the resources that is there and while foundational resources the foundational computer resources sharing would be a major challenge but i think a bigger challenge might be to manage the interdependence of the AI ecosystem because it spans hardware, software, and the protocols, or the ethics around that” [1]. “So I think one of the biggest challenges would be the governance around this sharing mechanisms, sharing protocols, and managing the framework” [2]. “And the other would be what would be the talent and the institutional capability, which is in a way required” [8]. “What is the capability and confidence in the systems that manage that we have the required methods to ensure that it takes care of the priorities and the values that each country wants to push forward?” [6]. “Well, the infrastructure can be acquired, but expertise has to be developed” [11].

Major discussion point

Governance and Coordination of International AI Collaboration

Topics

Artificial intelligence | Data governance | The enabling environment for digital development

Natasha Crampton

Speech speed

140 words per minute

Speech length

Role of Philanthropy and Public‑Private Partnerships

Topics

Financial mechanisms | Artificial intelligence | Capacity development

Peter Mattson

Speech speed

172 words per minute

Speech length

Measurement, Metrics, and AI Metrology

Topics

Monitoring and measurement | Artificial intelligence

Justin Carsten

Speech speed

81 words per minute

Speech length

1457 words

Speech time

1070 seconds

Systematic measurement as key to democratizing AI

Explanation

The moderator emphasizes that systematic, robust measurement and benchmarking are essential to democratize AI and ensure trustworthy outcomes for all stakeholders.

Evidence

“And it’s a great pleasure that one of the large organizations in the private sector, Microsoft, has got representation here” [94]. “And it’s interesting, there is a report that’s been released by ML Commons this week on robust and defensible benchmarks” [91]. “to echo the obvious point, which is that measurement is tremendously important” [83].

Major discussion point

Measurement, Metrics, and AI Metrology

Topics

Monitoring and measurement | Artificial intelligence | Financial mechanisms

Participant

Speech speed

158 words per minute

Speech length

Role of Philanthropy and Public‑Private Partnerships

Topics

Financial mechanisms | Artificial intelligence | Capacity development

Wendy Hall

Speech speed

156 words per minute

Speech length

1740 words

Speech time

667 seconds

Open data governance and need for global frameworks

Explanation

Wendy argues that while the open data movement is vital, not all data can be fully open; therefore, interoperable data‑governance frameworks, registries and cross‑border sharing mechanisms are essential for trustworthy AI development.

Evidence

“well there’s two things about that, the open data movement has been really important but not all data can be open it can’t be and I mean you can have data that is exchangeable shareable that won’t necessarily be open” [3]. “another thing I’m on is the UN, it’s the CSTD Commission for Science and Technology in the UN data governance working group… how can we actually get some you know how do we do cross‑border data sharing how do we get the data flows so we can actually share data sets” [3]. “we need to build data tell people where the data is we need data repositories or at least registries that’s around the world so researchers know where the data is” [3].

Major discussion point

Open Data and Data Governance

Topics

Data governance | Artificial intelligence | The enabling environment for digital development

Establishment of AI metrology institutes

Explanation

Wendy describes the creation of the UK’s Centre for AI Measurement and the AI Security Institute as steps toward a new science of AI metrology that can quantify trust and reliability.

Evidence

“the National Physical Laboratory announced two weeks ago backed by the UK government the Centre for AI Measurement and the UK AI Security Institute which was founded by Rishi Sunak at Bletchley Park” [88]. “we can do it we don’t have there is time to do this the world is not going to end at the end of this year because of AI” [89]. “one of the metrics in AI metrology will be the trust factor” [89].

Major discussion point

Measurement, Metrics, and AI Metrology

Topics

Monitoring and measurement | Artificial intelligence | Data governance

Agreements

Agreement points

Measurement and evaluation are critical for trustworthy AI development

Speakers

– Peter Mattson
– Natasha Crampton
– Participant
– Wendy Hall

Arguments

Arguments

Talent and institutional capability development is critical for democratizing AI globally

Countries that adopt and diffuse technology fastest succeed with transformative technologies, with skilling as a major unlock

Summary

Both speakers agreed that building human capabilities and skills is essential for successful AI adoption and diffusion globally

Topics

Capacity development | Artificial intelligence | Social and economic development

Building confidence and security in the use of ICTs | Artificial intelligence

Unexpected consensus

The primacy of trust over technical capability in AI adoption

Speakers

– Peter Mattson
– Natasha Crampton
– Participant

Arguments

Reliability, not capability, is what’s holding back AI adoption today – people need to trust AI with sensitive information

Trustworthy AI diffusion requires active choices and interventions rather than happening naturally

Trust requires AI systems to be accessible, work in the right language, and be relevant to local contexts

Explanation

It’s notable that speakers from different sectors (technical benchmarking, corporate, and development) all converged on trust as the primary challenge, rather than technical limitations or resource constraints

Topics

Building confidence and security in the use of ICTs | Artificial intelligence

The need for sophisticated measurement approaches across different domains

Speakers

– Peter Mattson
– Wendy Hall
– Participant

Arguments

Industrial-quality benchmarking with common yardsticks is necessary for industrial-level AI reliability

A new science of AI metrology is needed to measure the effects of AI systems including trust factors

Real-world evidence is crucial to prove AI’s actual usefulness in social and development sectors

Explanation

Unexpected convergence on the need for rigorous measurement methodologies from speakers with very different backgrounds – technical benchmarking, academic research, and development practice

Topics

Monitoring and measurement | Artificial intelligence

Overall assessment

Summary

The speakers demonstrated strong consensus on several key issues: the critical importance of measurement and evaluation for trustworthy AI, the necessity of collaboration and partnerships, the need for local adaptation and cultural sensitivity, and the fundamental role of skills development. There was also notable agreement that trust and reliability, rather than technical capability, represent the primary barriers to AI adoption.

Consensus level

High level of consensus across diverse stakeholders from different sectors (academic, corporate, development, technical). This suggests these priorities are well-established and could form the basis for coordinated action. The convergence on trust as the primary challenge and measurement as the solution indicates a mature understanding of AI deployment challenges that transcends sectoral boundaries.

Differences

Different viewpoints

Approach to AI model development – centralized vs. decentralized

Speakers

– Dr. Saurabh Garg
– Participant

Arguments

More efficient, domain-specific models can reduce compute and energy requirements, widening AI diffusion

Are they large centralized models? Or are they dispersed decentralized models on the edge?

Summary

Dr. Garg advocates for more efficient, domain-specific models to reduce costs, while the Gates Foundation participant questions whether large centralized models are the right approach and suggests considering dispersed, decentralized models on the edge for better accessibility in areas with poor connectivity.

Topics

Artificial intelligence | Environmental impacts

Data openness vs. data governance complexity

Speakers

– Justin Carsten
– Wendy Hall

Arguments

Open data and data sharing are important components of trustworthy AI development, building on established movements for openness

Not all data can be open, but exchangeable and shareable data frameworks are needed

Summary

Carsten emphasizes the importance of open data for trustworthy AI, while Hall argues for a more nuanced approach, stating that not all data can be open and advocating for exchangeable and shareable frameworks instead.

Topics

Data governance | Artificial intelligence

Unexpected differences

Gender inclusion in AI governance discussions

Speakers

– Wendy Hall
– Other panelists

Arguments

Women and children are excluded from AI discussions despite being 50% of the population affected by AI safety decisions

No direct counterarguments, but implicit exclusion through male-dominated discussions

Explanation

Hall’s critique of gender exclusion in AI governance was unexpected as it challenged the very composition and approach of the panel itself, pointing out that the discussion about inclusive AI was itself not inclusive in terms of gender representation.

Topics

Human rights and the ethical dimensions of the information society | Closing all digital divides

Measurement approach – technical vs. social science

Speakers

– Peter Mattson
– Wendy Hall

Arguments

Industrial-quality benchmarking with common yardsticks is necessary for industrial-level AI reliability

A new science of AI metrology is needed to measure the effects of AI systems including trust factors

Explanation

While both advocate for measurement, Mattson focuses on technical benchmarking while Hall calls for a broader interdisciplinary approach incorporating social science methods. This represents an unexpected divide between technical and social approaches to AI measurement.

Topics

Monitoring and measurement | Artificial intelligence

Overall assessment

Summary

The discussion revealed relatively low levels of direct disagreement, with most conflicts being about approaches rather than fundamental goals. Key areas of disagreement included technical approaches to AI development (centralized vs. decentralized models), data governance strategies (open vs. controlled access), and measurement methodologies (technical vs. interdisciplinary). The most significant tension was around inclusivity, with Hall challenging the male-dominated nature of AI governance discussions.

Disagreement level

Moderate disagreement level with significant implications for AI democratization strategies. The disagreements suggest different pathways to achieving trustworthy AI, which could lead to fragmented approaches if not reconciled. The gender inclusion critique has particular implications for legitimacy and effectiveness of AI governance efforts.

Partial agreements

All speakers agree on the need to democratize AI globally, but disagree on the primary mechanisms. Crampton emphasizes private sector investment and infrastructure, Garg focuses on talent development, and the Gates Foundation participant advocates for open source solutions for affordability.

Speakers

– Natasha Crampton
– Dr. Saurabh Garg
– Participant

Arguments

Private sector investment is necessary due to the scale of infrastructure needs that cannot be met by governments alone

Talent and institutional capability development is critical for democratizing AI globally

Open source models are critical for governments in the global south that cannot afford expensive proprietary solutions

Topics

Artificial intelligence | Financial mechanisms | Capacity development

All speakers agree that measuring and ensuring AI reliability is crucial, but emphasize different aspects. Mattson focuses on technical benchmarking infrastructure, Crampton on active governance choices, and the Gates Foundation participant on real-world evidence in development contexts.

Speakers

– Peter Mattson
– Natasha Crampton
– Participant

Arguments

Industrial-quality benchmarking with common yardsticks is necessary for industrial-level AI reliability

Trustworthy AI diffusion requires active choices and interventions rather than happening naturally

Real-world evidence is crucial to prove AI’s actual usefulness in social and development sectors

Topics

Monitoring and measurement | Artificial intelligence | Building confidence and security in the use of ICTs

Both speakers agree on the need for localized AI systems, but Crampton focuses on linguistic and cultural adaptation while the Gates Foundation participant emphasizes policy and regulatory context adaptation.

Speakers

– Natasha Crampton
– Participant

Arguments

AI must work in local languages and cultures to be meaningful in people’s lives

Context-specific AI tools must work within local policies and cultural frameworks

Topics

Closing all digital divides | Artificial intelligence

Building confidence and security in the use of ICTs | Artificial intelligence

Takeaways

Key takeaways

Trustworthy AI requires reliable systems, not just capable ones – reliability is the primary barrier to AI adoption as people won’t trust AI with sensitive information without it

International AI collaboration faces major challenges in governance of sharing mechanisms, talent development, and managing interdependencies across hardware, software, and ethical protocols

AI democratization requires massive infrastructure investment ($50 billion by Microsoft alone), with private sector involvement essential due to scale requirements beyond government capabilities

Cultural and linguistic adaptation is critical – AI must work in local languages, respect cultural contexts, and solve local problems to be meaningful and trustworthy

Skills development, particularly teaching educators, is a key unlock for AI diffusion, as countries that adopt technology fastest historically succeed with transformative technologies

Industrial-quality benchmarking and measurement systems are essential for building reliable AI, requiring common yardsticks across safety, security, and correctness dimensions

AI development is heavily male-dominated, excluding 50% of the population (women) from decision-making about AI safety and governance

Open source models are crucial for global south countries that cannot afford expensive proprietary solutions, helping prevent digital divides

Data governance is being ignored in AI governance discussions but is essential for cross-border collaboration and trustworthy AI systems

Resolutions and action items

Microsoft committed to spend $50 billion by end of decade on AI infrastructure in global south with five-pillar approach (infrastructure, skilling, multilingual AI, local innovation, data sharing)

Microsoft committed to train 2 million Indian teachers in AI-specific skills through partnership with Indian national standards and training institutions

ML Commons and Microsoft announced collaboration on expanding safety benchmarks to include Hindi, Tamil, Malay, Japanese, and Korean languages

Lingua Africa initiative launched to collect local data with communities for spoken languages in partnership with Gates Foundation

Leading AI companies signed New Delhi Frontier AI commitments to contribute adoption data to World Bank-led projects for policy makers

UK established Centre for AI Measurement at National Physical Laboratory and joined Network for AI Measurement and Evaluation

Need to develop more efficient, domain-specific AI models to reduce compute and energy requirements

Unresolved issues

How to manage direct conflicts between different jurisdictions’ AI laws and regulations while maintaining global interoperability

Scaling industrial-quality benchmarking systems globally while maintaining technical rigor and cultural sensitivity

Determining optimal balance between large centralized models versus dispersed decentralized edge models for different use cases

Establishing cross-border data sharing frameworks and governance mechanisms for AI development

Measuring real-world evidence of AI usefulness and impact in social and development sectors

Addressing the gender imbalance in AI decision-making and ensuring truly inclusive AI development

Creating sustainable funding models for AI infrastructure and capabilities in global south beyond initial investments

Developing comprehensive AI metrology science to measure trust factors and societal impacts

Suggested compromises

Building AI systems with configurable controls and choices that allow downstream adaptation to local contexts and values while maintaining core functionality

Using federated evaluation approaches that enable testing across diverse datasets while respecting legal and sovereignty boundaries

Implementing sovereignty controls in data centers and offering both public and private cloud options to balance global access with national agency

Making models available as open source or open-weight to empower ecosystem adaptation while maintaining some proprietary elements

This comment introduces a completely different technological paradigm – moving beyond traditional digital computing architectures. It’s thought-provoking because it suggests that true AI democratization might require fundamental hardware innovations, not just better software or governance models.

Impact

While this comment didn’t immediately shift the conversation, it planted seeds for thinking about AI accessibility from a completely different angle – hardware innovation rather than just policy or software solutions. It added a layer of technological speculation that broadened the scope of what ‘sovereign AI capabilities’ might mean in the future.

Overall assessment

Explanation

Developing quantifiable trust factors as part of AI metrology is crucial for creating reliable and trustworthy AI systems.

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.