AI for Social Inclusion: Opportunities, Risks, and Policy Priorities (HLPF Side Event)

Good afternoon, ladies and gentlemen.
We're about to start this discussion.
Good afternoon, ladies and gentlemen.
We're about to start this conversation.
Let me, on behalf of the UN Department of Economic and Social Affairs and the permanent mission of the Philippines, welcome all of you to this discussion on AI for social inclusion, opportunities, risks, and policy priorities.
I want to thank the Per Mission of Philippines joining hands with us in focusing on the very topical theme, but it's also extremely important for us because I hope all of you know DSA produces the Annual World Social Report, and we want to use conversation today in shaping our reports chapter on AI.
I'm giving you a preview of our report, which will be released later this year and the report comes out next year also.
Both reports will look at the challenge of inclusion in the age of AI.
I think nobody can question that AI offers tremendous opportunities.
Are there any dissenters? Raise your hands if you think it doesn't.
We all agree.
It has tremendous opportunities for human society, both making firms more productive, more responsive public services, better access to health information and diagnostics, expanded access to higher quality education, and not to mention the tasks it's performing in terms of research and analysis for us.
But These benefits are not going to happen on their own accord.
These are not automatic.
It's not a given.
Without inclusive policies, AI can and will reinforce his biases.
It will widen inequalities and deepen existing exal divides.
The mode will grow if we do not work in building bridges to address these gaps.
This is why social inclusion must be at the center of our approach in shaping AI in all societies, developing and developed.
It's not a north North is also facing challenges in terms of exclusion and growing inequalities.
So AI policy and social inclusion must go hand in hand.
Investments in digital infrastructure must be matched by investments in people, in education, lifelong learning, in digital and AI literacy, in social protection, and institutions that build trust and safeguard rights.
But access alone is not enough.
What matters is whether people have the skills and opportunities to shape, not simply shaped by it.
The task before us is to choose between innovation and inclusion.
That's not to choose either or.
It is to shape innovation so that inclusion is built into the way it is developed, governed, and used.
That is what we are going to focus on today.
Inclusion should not be an afterthought, should not be a sideshow.
It should not be treated as something that you pursue once you have achieved the AI skills.
It should be a central objective of our policies.
We look forward to hearing from our distinguished panelists.
I will introduce them and I invite them to speak.
And of course, you, from all of you, your thoughts, your reflections that you can share with us.
But before I do so, let me invite our co organizer, the Deputy Permanent Desnt of the Philippines, Henrique to share his reflections with us.
Over to you.
Thank you.
Thank you so much, ASG Hanif.
Good afternoon, Excellencies, distinguished guests, colleagues, and of course, our panelists this afternoon.
Thank you for taking the time to be with us.
It's really a great honor and privilege for the Philippines to co host this event with esa, our longstanding partner, and to bring this important conversation really to the center stage of discussions here at the HLPF.
We meet at a pivotal juncture.
AI is rewiring and rewriting the realities of our economies, our governance structures, and our societies today.
The Philippines this year is chair of the Association of Southeast Asian Nations or Asean, and our theme for the year is navigating our future together.
In this context, we are focused on bringing and bridging and building a forward looking, resilient, and above all people centered region.
AI increasingly plays an important role in this regard.
Experts estimate that AI adoption could contribute up to ten to 18% of GDP across the Asean region by 2030.
But harking back to the topic of our side event today, AI for social inclusion, opportunities, risks, and policy priorities, we must also ask the more difficult questions.
These are who stands to benefit from this growth and perhaps more importantly, who risks being left behind.
As ASG Hanef mentioned, the opportunities for AI for social inclusion are immense.
AI can bridge gaps in public service delivery, modernize climate resilient agriculture, and revolutionalize healthcare, especially in remote island communities, for example.
Yet the risks of inaction or unguided development are equally Stark, if left unchecked, AI threats to exacerbate existing digital divides, introduce algorithmic biases that reinforce historical inequities and biases, and expose our most vulnerable populations, particularly women and youth to unprecedented digital harms, data misuse, and deep fakes.
True social inclusion then would have to recognize that technological progress without an ethical anchor can be a dangerous engine for inequality.
We here at the UN have been starting to institutionalize and develop guardrails against this.
Recent documents and mechanisms such as the Global Digital Compact, the global Dialogue on AI global governance, and the Independent Scientific Panel.
All these processes are very important, but in many ways, they're only the tip of the iceberg.
To make AI more inclusive and a force for social inclusion, much more really needs to be done.
In the Philippines, just allow me to share some examples of how we're taking a proactive approach to shape a more inclusive digital future for our people.
First, it's through strategic and centered frameworks.
We have produced and rolled out our national AI governance framework, which is tied to our national AI strategy Roadmap, 2.0, and this framework establishes a human centered, human rights based approach to ensure AI adoption safeguards, human dignity and data privacy.
Second, through institutionalized and equitable research.
We work to ensure that we are creators and not just consumers of the technology.
So the Philippines has established a center for AI research, which is tasked with finding local and public good applications for AI, such as weather modeling for disaster response and ensuring that the products of technology directly benefit rural communities.
And third, through grassroots literacy and upskilling, we recognize that connectivity is more productive with enhancing capability of our people.
We're training local government information officers and community leaders in the ethical use of AI, transforming AI from being an elite concept to a practical tool for regional and rural development and empowerment.
So distinguished colleagues, we think there should be two priorities, prioritizing digital literacy and upskilling and also enhancing inter regional and international cooperation.
Asean has a guide on AI governance and ethics, which provides a strong regional blueprint, but we must also align regional frameworks and mechanisms with the global mechanisms and frameworks to ensure coherence and more support for developing and emerging economies.
True innovation is not measured by the sophistication of algorithms, but by the breadth of their benefits.
A metric for successful AI can be how effectively it uplifts the marginalized, reaches the underserved, and empowers the vulnerable.
Thank you once again to you and Tessa, especially A haf and to all our distinguished panelists and speakers and all of you today in attendance.
Thank you for your partnership, and we look forward to an enriching discussion.
Thank you.
Thank you.
Enrique, for your very thoughtful remarks, I must say, and you also shared how Philippines is grappling with these challenges.
I think this has given me a very nice segue to what the UN is doing.
I must say UN was a bit ahead of the curve since 2017.
In every statement that the SG has made to the GA in September, he highlighted the challenges that AI will pose to human existence.
We are just focused on these uses.
Let's not forget the uses and weaponization of AI.
These are very serious challenges we are confronted with, but UN has an independent panel which has recently issued a report.
UN also organized a dialogue on AI governance in Geneva last week.
I want to share a video on that dialogue to share with you the messages coming out of the dialogue on AI governance.
Please show the video.
No future builds itself.
And so the choice before us is not between facing AI or fear of it.
It is between governing by design and drifting by default.
The global dialogue is about civilian AI, but AI does not respect that line.
The same models and chips have moved into the battlefield.
My main concern is with little autonomous weapons systems.
Let us call them what they are killer robots.
We refuse to let our platforms become battlefields.
It means us as adults, as governments, as policymakers, as businesses to address the sinister uses of AI, such as deep fakes, which disproportionately target women and girls.
In every high stakes decision, machines can inform, but humans must decide and answer.
So very powerful message from our leadership, also, President of the General Assembly and the Secretary-General.
The punch line is human beings should remain at the center of these developments.
We should steer it to our advantage, not to the disadvantage of humanity.
So as I mentioned, a distinguished panel that is going to share their reflections.
I want to introduce them briefly.
Miss Yan Fu, Professor of Technology and International Development at the University of Oxford, and a member of the UN High level Advisory Board on Economic and Social Affairs.
I had the honor of working with her for some years and it has been a pleasure.
Then we have Mr.
Vijay Moody, Professor of Mechanical Engineering and of Earth and Environmental Engineering and Director of the Lab for Sustainable Energy Solutions at Columbia University.
Thank you for joining us.
Mr.
Naveen Gautham, senior legal researcher at the Global Forum of Communities, discriminated on work and dissent.
As also our youth representative in this panel.
Thank you.
I will begin this discussion with a broad question, but each panel is getting 1 minute to respond, and then I have a specific question for each panelist.
The overarching question is for all of you, but let me ask, where do you see AI already helping people who have been underserved by existing systems.
It is being inclusive.
Where do you see it? It is reinforcing exclusion and the current disparities in societies.
Let me begin with Chalian, over to you in a minute to respond.
Thank you.
Thanks, investor and also Asean Secretary for inviting me to this very important meeting.
I think the topic is very important and timely.
I go straight to the question to answer the question, where do I see the opportunities and where is the risks? First, I give two examples about inclusion.
One is about AI in health.
Now AI in health, accelerated discovery of drugs much, much faster than the traditional ways.
Also during the pandemic, AI has been used to screen all the CT scan images and identify patients who are affected by COVID and severe diseases and that helped accelerate the diagnosis and also help people and regions who are underserved without sufficient medical professionals.
This is what we experienced during the pandemic.
And another example is what my personally participated in an action research, which is using AI to evaluate the potential of ideas created by researchers, startups, by young people.
This is an area which has a significant barrier and startups and researchers and youth cannot afford to pay high costs to do the valuation of their ideas for technology transfer and commercialization and set up startups.
This AI enabled to helped us to do this in a much objective way.
First, is make it democratic rather than investors say it, decided.
Secondly, it's much faster 2-3 months to 10 minutes and the cost is less than one tenth.
So many we have served more than 20,000 startups, and also now we are using it to help the university graduates.
We know that youth unemployment is another pressing issue, and many youth have their ideas.
Of course, some great ideas need to be ident, you know, to be recognized and being supported, and some have ideas maybe not that mature and need some advice to improve or some, you know, the should be advised not to jump into not that mature idea.
This is an area underserved and this AI tool can help the students, help the universities to provide career and employment advices to the students.
A lot of students being able to give in the advice and then choose the right direction, choose the right opportunity.
This is the opportunities, and of course, there are gaps.
One of the gaps that I have seen is between countries and between regions, the students and people who do not have access to broadband, even broadband to use AI.
Also in our universities, some richer students use more professional version of GPT, and students from disadvantaged backgrounds can only use basic GBT.
That create inequalities too.
The letter known students in backward regions.
These are the examples about the opportunities and risks.
Of course, the implication is clear.
I may come later and leave the float.
Thank you.
Excellent, very precise and focused.
Thank you so much.
Mr.
Navin got them over to you to this overall question, where it is serving and where it is excluding forward.
Yeah, I'm horrible.
Thanks.
I would focus more on where these things have been excluding altogether because we have been having a lot of discussion around why AI is more of a positive aspect.
But when you actually look from a lens of a person who has been facing similar forms of discrimination constantly and how AI accessibility itself is a challenge for me because once you create a digital divide, which got created back during COVID, now there is a major digital divide is further being created because of the AIA as well, because technology actually depends on those who have access to technology.
And most marginalized groups, especially working descent based discrimination, those who are facing the indigenous communities as well persons with disability and also migrants and refugees, they have been kept away even for access to technology aspect as well.
But all of us know that it all depends on digital literacy.
Even you have access to the technology, then it will depend on digital literacy as well, whether you have access to digital literacy or not.
Another level comes up when we talk about access to, uh, why AI is, how it can be useful.
One major thing which I have learned in the past is that these challenges are because it's AI often is not able to differentiate between what exactly marginalization is, what exactly vulnerability is, and what ends up as a discrimination which may be in the mind of one person, but may not be a discrimination for other person.
If you write about anything on the CGPT what my co panelists has also referred to and other applications as well, other AI apps as well.
There is no specific mention of what cast based discrimination exactly is.
It's very vague.
And they say the terminology is being used as maybe facing these forms of discrimination, but for us, we do face these forms of discrimination.
That creates a biasness moderator in this sense.
Especially in terms of recruitment, education, credit housing, and social protection, there are strong examples of biasness which are being created by AIN.
It's not a critical assessment.
It's just like these other things where we can always map out and see where we need to have strong improvement.
Just to quote, International Labor Organization has also highlighted that labor market disruption driven by AI is likely to disproportionately affect women and young people around global South.
Within that global South, there is another South in the countries which are facing similar forms of discrimination, kept away from literacy, kept away from digital literacy, kept away from technology as well, which unfortunately is leading to what we call as digital colonialism, enhance a few people and other people who are facing working in descent based discrimination are always they are kept behind and Again, I say I'm not criticizing AI.
I'm just saying these are the gaps where we need to fulfill where AI can be very useful if we use it in a proper manner as well.
Just to quickly give a few strong examples, I thought I'll just bring it to you.
These are real life examples of workers, basically.
Factory workers in India were actually asked to wear head mounted cameras while they are performing the work.
These are most of the unskilled workers so that AI can actually capture what they are doing and later on they are replaced by AI itself.
They don't have any other option left because that's the only livelihood options they basically have.
And what happens like many workers were not fully informed that the recordings could be actually used to train commercial AI aspects.
Look at the way how we are looking at something which can be improved so powerfully through AI, but on the other hand, the way it has been used is quite exploitative in manner.
These are the few gaps I thought I'll just present so that we can see how do we move forward with this.
Thank you.
You coming from a young person, what you are saying is it is exacerbating the existing divides and challenges.
But I will come back to you the fact that it's a reality we have to deal with.
We also need to look for solutions.
I'm afraid we can't simply wish it away, not the times.
Advocating for it, but it's been coming our way faster than we expected.
So please think about it when I come back to you.
We over to you this overarching question on exclusions and inclusions, underserved and those excluded, your thoughts? No.
Thank you.
I'm more a field person, not a computer scientist and work with governments.
I feel AI is just another layer of dependency on large corporations that I see.
It didn't start so much with hardware, but with software, with databases, with large systems.
I saw that governments were over promised and highly overcharged for services that initially they were told would be low cost.
The recurrent cost and dependency were not disclosed.
I worry that AI will create yet another dependency on what is becoming a monopolistic enterprise.
I just want to be clear where I'm coming from.
Now I'll give you some examples, okay.
So, lots of satellite data.
I'm an engineer, started to get collected at a very high resolution, ten meter resolution, eight bands about, I would say ten years ago, which was public data, open data, but these AI companies started to use it and then sell products or sell or tell many emerging developing countries that, hey, we can do this for you, we can do this for you.
And interestingly, they had never been to the field to collect any ground truth at all.
They started to sell and train models.
As a result, two things happened.
First of all, they got it wrong because they didn't even know how to evaluate whether they were right or wrong.
They never involved the people in the country to check, right? Thirdly, they ended up predicting right.
I worked quite a bit in rural areas, not that urban is not important, just that's where I work.
They ended up focusing on large farmers because that's all they could do.
They could do very well ten hectres, five hectres.
But the poor don't own five and ten hectas.
They own a tenth of a hecta.
Government was very keen to help bring energy to where it was needed.
Energy infrastructure is expensive, so estimating demand on the field correctly and well can save billions of dollars.
So we were asked because of trust we had built in over the years doing mundane things really not AI.
Just in this example was Uganda government.
They said, you know, what do you think? And I said, look, there's no ground data.
For one fifth of the cost of what some international partners said they were bringing in, the government deployed 100 young people on motorcycles to collect field data across the whole country.
80,000 farmers were interviewed for less than one fifth of what AI had promised.
But now AI can deliver if you have this.
Right? So I want to highlight the importance that you can exclude vulnerable smallholders.
It turned out we were not looking for this when we collected data.
It turned out they were women.
It turned out they were focusing on horticulture and nutrition.
It turned out they were doing the right thing, but the government didn't know that to be able to then help amplify the innovations that the poor themselves were doing, right? So I think AI can be of great value.
My my observation was that Who owns the training data is actually going to be extremely important.
I think countries should watch out for this.
The every bit of it, their own students can do at the level that these big companies are promising.
I think we have to be at least for some of the work where I'm worried about field infrastructure, how to leverage decentralized technologies, detecting what people actually want, where livelihoods are at stake, where food and nutrition is at stake, We need cutting edge tools, but we also need data and that should be owned and collected by the countries themselves.
O.
Thank you so much V.
I think the fundamental issue is governance because monopolistic big corporations top down will not serve societies and governance remains the key.
Let me come to specific questions for each panelist.
But thank you for sharing your initial thoughts, which have been very powerful, I must say in capturing all dimensions.
Sin, my question to you because you have done so much work.
How can developing countries embrace AI in ways that support structural transformation and inclusion? Because middle countries are struggling for structural transformation.
Good AI could help them, but how or should they use it to you.
Many things.
I think AI brings opportunities and challenges and risks.
First is the developing countries, the middle income countries should try their very best like Russian secretary and Ambassador Garcia has mentioned, try their very best to try to benefit from AI and harness the benefits for sustainable development goals.
That's number one and also, minimize the risks and turn challenges, crisis into opportunities.
I have three key messages here I want to make.
First is, it is important and is totally right to emphasize the risks and also the governance of AI and also emphasize that human should make the decision and AIs are too.
However, I worry that we have under emphasized the use of AI, and how developing countries should embrace AI and benefit from it.
Why I say so is the data shows that the diffusion and adoption rate in global north and south already is a big gap there.
In the developing countries, including China and India, the adoption rate, according to a recent report is 15% in comparison to 26% in the developed countries, of course, some countries are as high as 60%.
So this is including China and India, exclude China's adoption rate, and then the other part of the developing countries, low income countries is less than 5%.
We have not even used AI, not even benefit from it.
I think the first action is to really create the capabilities, the infrastructures, the needed regulatory frameworks and policies to benefit the good side.
Of AI.
That's one I want to emphasize.
Secondly, it's about AI.
AI is a broad term.
There are many different technologies and many different applications.
Now, one of the reports suggests the most frontier AI is becoming learning how to make decisions themselves and they are more risky.
However, that's the frontier of AI, but there are many matured AI, which we know well.
They are responsible, transparent, an inclusive AI like AI for health, AI for education, AI for, you know, climate change.
They are not that advanced but appropriate AI, inclusive AI.
We should focus on that and relocate resources to use those AI rather than the companies and the countries compete at that kind of risky frontier AI.
This is kind of differentiate different types of AI and use those inclusive and responsible AI and reallocate resources on this and not in the competition of the risky frontier AI, which we don't know, which is a black box.
I think that's the second point I want to make.
The third I want to say is really the developing countries need international collaboration and the international organizations and regional organizations should play active role to help the developing countries to build up their skills capabilities, infrastructure, which is very capital intensive, they need funding, and also governance which is UN is leading, which is totally correct.
And in this regard, Acean can play important role to coordinate the Acean countries and African Union and also Latin American regional organizations can also help the countries and UN can coordinate globalize North South collaboration.
Some of the examples, in addition to the ambassador has introduced Ocean's AI Center, like in China, there are big country AI center that help the brick countries to build.
Also there are some south south collaboration AI capabilities center.
These are the examples that the global community can work together helping the developing countries.
Otherwise, because of the infrastructure constraints and also the skills gap, then the developing countries will lack even behind.
I think at this moment, we should, on the one hand, emphasize governance, at the other hand, really work together, help the developing countries to use AI and embrace the positive, positive force brought by AI.
Thanks.
Thank you so much, Chen.
The message is prepare policy environment, governance, regulatory measures.
The embrace, don't go for advanced AI, to embrace what is available and useful.
Then third, cooperation, regional, global, national to pursue this.
Thank you so much.
With over to you, you just mentioned you have been in the field.
AI depends on physical foundations, reliable electricity, connectivity, and computing power that many countries still lack.
What will it take for developing countries to build these foundations so that AI narrows rather than widens the divide that they're facing right now? Great question.
I have somewhat of a contrarian view on this.
See, historically, we're really dependent on large interconnected infrastructure for electricity.
So we thought we must do that before we do other things.
However, the game is changing.
It is possible now to have far more decentralized infrastructure that actually is frequently coming out to be more cost effective, but more importantly, faster than centralized infrastructure.
It is possible, increasingly, and I'm supporting Professor Fool's comment also, There is computing hardware also available at lower cost at the scale that is needed to add that decentralized infrastructure.
I also think that in education, in training, to use some of these things, you don't need to build fancy laboratories with scanning tunneling microscopes and nano stuff, this or that.
Actually, I see this If governments take the lead, if we support them to get there, this can be done without as much dependence on the large physical infrastructure that currently hyperscalars and all these data centers they talk about here.
I think that in the field, many we also need to focus on yesterday, I was at another event where close to 70 80 countries spoke and half of them used the word inclusive.
I think inclusive has so many shades of meaning, but I'll take.
One is, I think that there are hundreds of millions of people where the households are making less than $100 a day, a, $100 a month for a whole household as opposed to 100,000 somewhere else.
The questions they are asking are different from the questions that the 100,000 a year people are asking.
I think inclusive means asking inclusive questions and addressing those inclusive questions.
I feel if we steer the young people towards some of these tools, lessons that can be developed without huge university expenses and infrastructure that other physical sciences need.
I see that other side as an opportunity to unlock this without having to build all the infrastructure first and a little bit of what I'm talking about maybe called leafrogging over.
Thank you so much, Anna I couldn't agree more with you, but what about human capital? I am talking about human capital.
How do we develop human capital Without dependence on the large centralized infrastructure.
Sorry to interrupt.
But I was actually saying that is the opportunity.
But I'm saying that opportunity is closer at hand in many countries because training people in these skills, I feel will be easier than training in nanotechnology.
Because nanotechnology depends on all these other ancillary things.
I'm saying that there has been somewhat of that democratization, if I can use the word using decentralized, smaller, but more pervasive.
Technology.
I can basically provide a megawatt with three, four megawatt hour of storage and a megawatt of solar for less than $0.10 per kilowatt hour in rural Zambia.
If I do centralized, it's coming out to be two times Thank you so much.
We're coming to Mr.
Naveen Gotham.
Navin, the question is from your perspective of young people and communities facing discrimination, what would it mean for AI policy to be genuinely inclusive? You had made some opening remarks, but we want specific policy proposals where we make sure it's public sector has that design to make it inclusive.
Over to you.
Shia.
I think I've criticized the e a lot, but the reality is I've already done this research through itself, that's another harsh reality.
Just like computers and Internet, when they came, there was so much of discussion happening.
It's going to stay forever, I believe.
We just need to see how do we look at the positive aspects sometimes as well.
But especially as I have been constantly Trying to focus on why there is another level when it comes to those communities which are most marginalized facing working descent based discrimination as well as those from the indigenous groups as well.
Digital literacy has been one major thing, which we should always ensure that it is not in hands of a Even within the youth, there is another level where specific set of youth have access to these technology and digital literacy.
But for the youth who have been facing these forms of discrimination, they have always been often kept out of this thing because we don't have access to technology.
This is one part A, how do we recognize datasets on work and descent based discrimination, indigenous group? Because I think co panelists, Mr.
Via also very specifically mentioned, how do we go on the field and identify these realities? Instead of having this in the hands of few corporates and this corporate also has people who belong to the dominant communities across the global south.
This is not only about global South, I repeat, this is also happening global North.
Recognition of datasets specifically on work and decent based discrimination, indigenity, migration refugees, how many refugees, young people who have access to this is another major aspect.
Access to training has already come up a number of times.
I strongly support that thing as well.
But we need to see how the accessibility reaches to the youth because for me, who's from the same community facing similar forms of discrimination.
It's also a technological aspect, a challenge coming up because I also sometimes face issues in terms of accessing AI.
But if I look at other youth on the same group, but not from the same community, also from the dominant, they also have a strong understanding of AI where they're able to use it very properly.
Training is another aspect.
Meaningful participation and fair representative datasets.
How do we ensure that the AI is not specifically in the hands of a few people who are dominating the market? Instead, youth from the marginalized groups, historically marginalized groups have access to that and they're also in the decision making process.
It should not be like we have some people who are designing AI, but the youth who are facing these forms of discrimination don't even have an understanding of what's the basis of AI.
How do we bring those youth specifically and ensure that we have a strong understanding and they are into the decision making process as well.
I think the final thing which would come up is most important thing in terms of the artificial intelligence, which we are missing out right now is, how do we respect local knowledge? How do we respect indigenous knowledge? How do we respect the knowledge which communities which are facing discrimination based on work and descent? How do we ensure that their knowledge is being embedded very strongly, which ultimately doesn't create a bias when anybody goes online and looks for the AI aspect.
I've already very specifically told what's happening with the workers across India and most of the workers are from the marginalized groups excel.
These are the few specific recommendations.
If you'd like to also comment on the youth unemployment because we are to witness recent graduation ceremonies.
Most of the graduates were questioning AI because they are not finding jobs in the job market.
What is the youth intake on that? I think there are two different versions, I would say on because there is a version which comes from the dominant communities.
Uh, where they are facing these forms of issues and they are not able to get employment.
Obviously sometimes it's AI.
There is another version which says many youth are getting employment with the help of AI as well.
That's also another aspect.
But from me, from my version, definitely from the communities which we are coming from, the employment is often taken away because of AI, from our people, which is a reality.
We are being asked to use AI AI trains itself through us.
As I gave an example, workers are being asked to put a camera and then they see how the workers are doing it.
So unless and until we are part of the process in the decision making as well as designing the AI, ensuring that we are not being kept away from the AI after AI comes into existence.
That's why I'm saying there are three different versions on this thing as well.
That's my.
Go ahead.
Then we open the floor for questions.
I really want to echo what Rajev and Nviing has mentioned first is, I think I echo there is opportunity for leapfrogging and this AI business, although infrastructure is capital intensive, but most important is creativity, people's creativity, which is labor intensive and young people and people in both developed and many developing countries too, their creativity will be the main force and that creates the opportunity windows of opportunity for catch up and leapfrogging.
I call it a digital windows of opportunity for leapfrogging.
Of course, we need basic infrastructure and the young people will be the main force.
Where is the opportunity focused on the young people, the youth.
They will be the main force in any countries which in the future, if we see leapfrogging, I believe will come from driven by the youth in their country, of course, supported by government and by the society.
And so that is one.
Therefore, I think universities, again, should play a very important role in training and skills, not only training the students in the in the universities, but during the summertime breaks, they could be training centers for people from the society and our students could be ambassador.
They can train many people in their communities who are not students.
It's one plus N, and they can help to build up the skills in their communities and in the society.
I think university should play a very important role.
Of course, the course should be supported by the government and the private sector, not You know, the universities themselves face constraints, financial constraints, but they have human resources and they have lovely students who have a lot of agencies to do this.
Another thing I want to say is although there are opportunities, there is under debated area.
We discuss skills, we discuss infrastructure, we discuss governance.
However, a lot of inclusive AI innovations because they are new.
They are challenging the existing market structure.
They are addressing the gaps.
Therefore, they are challenging some of the existing players.
They face a lot of structural regulatory institutional barriers.
A young student creates some inclusive AI.
Actually, they are challenging the existing players in a given field.
They are young, they are new.
Actually, they face barriers from the existing institutions, regulations, and existing big players who, who use old way.
That's one area really for AI to benefit society, not only skills and infrastructure there.
We need societal regulatory system changes to enable it.
That's area on discussed.
Thank you.
Every technology has incumbents and insurgents and there's always a public fight.
Insurgents always question and challenge the incumbents, we saw in the 90s also.
Navi, do you have a point then I will open the floor to get the audience to engage.
No, I think you have already touched upon.
I just wanted to just bring this point to the audiences.
In India, there are different levels of education across South Asia as well.
University is one area where we are talking about how do we ensure that that knowledge also flows to the regional colleges and also the primary schools because that is the place where we really need to ensure that the communities are having access to that place.
It's a very strong example how university can capacitate but that also needs to create an umbrella.
It flows down like a water and it impacts the knowledge actually impacts the communities also who are facing the forms of discrimination because they are the most marginalized and they don't have access to sometimes even the education which is given at the university level as well.
They are also more dependent on regional colleges, which are pending, just to add on to what my panels.
Thank you.
Thank you so much.
One message is clear from three speakers, AI does not stand for automatic inclusion.
Inclusion will not happen unless we have policy design, governance, ethical framework, and targeted policy measures to include people in using, in creating, and in deploying it.
Let me open the floor, kindly introduce yourself before you ask this question because we don't have nameplates.
I will have to point my fingers on you, so please don't be offended.
But I already have four speakers.
Fifth one also.
We'll begin with you, please.
Please press the button so that he can give you the good.
Yeah.
My name is Schad, they're John Ab and there can call me Sachin.
I'm a dias representative of communities discriminated based of work and descent.
I wanted to come in with this perspective and bring in this perspective where our communities are underrepresented in terms of data.
As we all know, data is the building block of artificial intelligence and we are systemically discriminated against and are not represented.
Because AI is the future, basically we're being erased from the future.
Especially in the context of India, Mr.
Roja and the lack of data around belts and other marginalize communities, would you address that? Because that's one of the most major issues.
Thank you.
We will come back to the panel after taking a few questions, but a very pertinent question.
I hope you won't be surprised.
The panelists are from India and China, two southern AI countries.
Over to you.
Hello? Yes.
Thank you very much.
I'm Cecilia Moley.
I'm here behalf of International Federation of Business and Professional Women or BPW International, and I'm here specifically on behalf of our Canadian chapter.
Within Canada, we recently launched an AI for all strategy.
It's basically a high level framework to focus on economic growth and enabling business sectors to use AI.
However, it's fairly silent on other protection measures.
Um, so you spoke earlier, all of you about inclusion, and I'm really interested to hear more about how we can ensure that all voices are captured, in particular, women and girls of all ages.
We know that AI disparately impacts women and girls, and so I'd be curious to know your perspectives on how civil society such as our organization can help influence our government to ensure that those protection measures are captured.
Thank you.
Thank you.
Over to you, please.
Thank you for the meeting discussion today.
My name is Henry Mido.
I'm here representing the McGill Youth Advisory delegation from Montreal Canada.
My question is more focused on the stigma around AI.
In my current context as a university student, there's still an overhanging and negative connotation with regards to educational spaces towards the use of AI, where students who do use it, even when it's for ethical and productive reasons or ways, are still being labeled as lazy or incapable, et cetera and this can have a lot of damaging effects on the relationship that youth build with AI and can even disincentivize them from using it productively at all.
So through education, how can we reduce these negative stigma so that youth are more incentivized to use AI ethically and productively.
Thank you.
Thank you so much.
Over to you and then gentlemen, please go ahead.
Hello, everyone.
My name is Samar Thayer.
I am a team delegate with the Global Colab Network, organization of teens who meet weekly to help work on the SDGs.
My question to you is that we know that the data in our world is inherently biased because they reflect a biased world.
It's all people's opinions and even if datasets, there's someone recording it, it cannot ever represent the entire world.
So if our data is fundamentally unequal, how can we train AI to be unbiased and not build off of current biases and expand them further? Thank you.
Thank you for a very insightful question to you.
Hi everyone.
My name is Brian Dog.
I'm from Johannesburg, South Africa, and I'm here on behalf of the Juventist Justice Foundation, which is incredibly proximate to AI inclusion.
We work with young people, we work with young people to provide legal education and divergent support so that marginalized youth don't have to go into direct incarceration and are able to act in futures of their own.
In that vein, I wanted to ask the panel if they think that frontier AIs should play a role in legal systems, and if so, how do we balance the fear of exacerbating inequalities, but also expanding legal access? Thank you.
Please go ahead.
Both of you Yes.
All right.
Hi, Ro.
My name is Devera.
I'm representing Civicus Civicus is Civil Society organization based in the Global South.
I'm originally from Indonesia.
My question to you is that you raised a concern about indigenous knowledge and also language inclusion, which I think is often overlooked when we talk about the development of AI technologies.
How would you ensure that In the future, when there is a development of AI technologies, and we train AI, we make sure that language inclusions are part of the discussion, including indigenous languages that contain a lot of indigenous knowledge that will help us tackle climate change, help us with education system.
So based on your experience and also, if there is a solution to that, what would it be to make sure that training AI includes language inclusion? Thank you.
Hello.
Sorry.
My name is Nancy Beach.
I'm from YAPSF.
I'm originally from Jamaica.
My question was, how are we going to aim to build inclusive communities for AI integration without leaving vulnerable communities already vulnerable communities more vulnerable to potential misinformation or AI hallucinations as those are running rampant in our current models today.
Thank you so much.
Great.
So could we begin from here, please, you go ahead and then I'll come to all of you.
Thank you.
Hello.
Hi, my name is Elena and I'm also here on behalf of YSPF.
My question is, it's not really a question, but I would love to hear more about your guys' opinions on not youth, but specifically adolescents and younger children who use AI specifically when it comes to education since there's been a lot of research in the past two years, specifically 2025 and the current year 2026, on how younger generations have become I've had a declining rate of cognitive functions and essential skills like critical thinking and problem solving and there is a direct connection between that with AI.
I'd love to hear just more opinions specifically when it comes to elementary students and middle school students and how we can ensure that AI is used properly for them since they aren't properly developed either yet.
Thank you.
Thank you so much, please.
I think we've got the three on this side, that's it, and then we'll go to that side.
Please go ahead.
Hello, I'm De Potala junior.
I'm a youth representative and founder for Unity for Prosperity, a nonprofit that focus on essentially publicizing youth authors in the Philippines and bringing light into stories that are less to your question, no marketing your stated before by some of the panelists, a learning should target vulnerable populations as you stated before, and these vulnerable populations are oftentimes not fully representative, especially in the existence of digital literacy.
Could you please expand on some of the actions that are actually currently being taken to better this digital literacy, especially in the youth groups that receive less attention.
Thank you.
To you and to the Ld Hello.
My name is Ad Veth.
I am the founder of Govern which I'm here for.
We work basically to inform youth about ways to get involved in the local community.
In that role, I've gotten very involved with local governments, which leads to my question, which is the big theme for this panel is how we can get governments to respond quickly and effectively in making AI frameworks.
But how can we ensure that when there's very often cases where governments cannot respond quickly to very local issues, which should be solved very easily.
Thank you.
Thank you so much.
There was a hand there? No.
Yes, please go ahead and then we'll come around.
Good afternoon.
My name is Nicole Wever.
I'm from the Ministry of Education of Curacao.
Based on some questions that the other people had about education, I wanted to go further about the workplace.
AI is helping a lot of people in the workplace, but our youth, they come with the background of basic AI.
In the workplace, that people are happy that they're using AI because there's cost efficient and time efficient, but then the youth that goes into the workplace don't have the time to get the junior level knowledge that they need to get to senior level.
This is also something that could bring a gap for our youth when they go into the labor market.
How do we work on that aspect? Thank you.
The entry level jobs challenge.
Yes, please go ahead.
Good afternoon, everyone.
My colleague Marty and I will come representing incision, organization formed by surgical professionals and we have a couple of questions.
How can governments ensure that AR reduces rather than reinforces existing inequalities in access to health care and location? AA has enormous potential in healthcare, but many communities still lack access to essential surgical care.
How do we balance investment emerging technologies with investment in strengthening fundamental health systems? Also today, AI has become extremely important, extremely useful for everyone.
We can ask to AI anything and AI can give us an answer.
So regarding the regulations in AI in healthcare, how we can avoid AI cannot become a risk for, let's say, low income communities where people with no credentials, medical that they say they are medical professionals, but they don't have the credentials, using just AI provide health services.
Thank you so much.
Last question and then we come to the panelists.
Go ahead, please.
Hi everyone.
My name is Tyuthhi and I just had a question because I currently work on building AI models for women endometriosis, which is a chronic gynenological condition which is underrepresented currently.
So I've often faced is this idea or backlash from other individuals regarding the fact that you're using these AI systems within marginalized communities, especially women.
Um, despite the fact that they can reflect existing biases because women are marginalized communities within AI systems and data models.
But at the same time, I think increasing the use of AI in women's health care can increase the representation of them for future clinical datasets and improve AI models and women's representation.
I'm curious to hear your perspective on this balance and how we can address this without necessarily reflecting women in these existing biases, but also including the representation and increasing the representation within these clinical datasets.
Thank you so much.
There are no more questions.
Let me come back to the panelists.
These have been very useful, issues that you have raised are very germane to this discussion, so thank you so much.
Let me begin with VJ because First of all, if the young people around this room, the questions they are asking, we're also being asked by the young people in a rural school in Mali, we have actually achieved a lot.
I think I just have to say this is phenomenal, the questions you all have.
I am myself a university professor, so I know how we got things wrong.
See, we, um, We need to work we allowed in corporations to go to the student directly.
This is the device.
But what I'm observing in the marginalized communities, in the low income settings, where we are missing the issues of data is coming up, literacy is coming up.
Corporations tried going directly to the student.
It did not succeed.
It succeeded in the wrong way.
Instead of targeting young, they were marketing to the young.
I think my observation was that building the institutions, the schools, the clinics, the teachers, the building up capability at that level, right? So I think that's critical because that is what the young can be learning from.
They're getting literate from those people.
So I think we need to rapidly reach that layer.
The education, health system providers who can today the good news is the decentralization that I talked about in technology can allow them to really just like we did science experiments in school of some, you know, a candle blows out if you cover the lid and all that.
But there is so much that can be done today with digital tools that a school can teach their own young people.
I think you raised the question of human capital.
I think it's central and I think that we should focus on that level.
Otherwise, the question Elena asked about really addiction that we have to bring young minds can be a tremendous positive energy.
If the right tools are there.
I have to say that We have to fundamentally change our curriculums because it's such a massive force.
Just like physics and chemistry was a massive force of the last 200 years.
I think empowering the teachers, empowering that layer of institutions.
Lastly, I want to end by saying, I saw so many technologies becoming white elephants.
I think if AI can be used to maintain, diagnose, operate Because we have thrown capital only at problems and when I see capital, things fail and nobody knows.
I think we have to be careful and I think AI itself can help us if we structure it right to diagnose what's not working and dynamically fix it.
We need to make that as part of the DNA.
Final point is the digital literacy, very important, but I think we rapidly need to do that literacy for the decision makers in the governments because they are being approached by corporations.
If they who are representing the population cannot make the decisions in an informed way, we also have challenges.
Thank you.
Thank you so much, Mj.
Remember in the late 90s, we underestimated the risks of social media, right? It was all upside.
That's all I heard.
And look where we are.
I think we cannot underestimate, but I'll come in when I come to summarize this discussion.
Shaun over to you.
Thank you.
First, I want to thank all participants for the thoughtful questions.
Very good questions.
I will focus on 23 areas that I have firsthand experience.
First is about, I the question comment about whether target to use more AI enhanced healthcare in the women in the society and then increase its representation in the dataset, definitely is a very good proposal and idea and that can help us to have more accurate in more close to true populations analysis and algorithms.
The same for the dat questions in India needs to provide some targeted programs to enhance their representation in the data and then come to the worry about, in the data because of the structural system like certain communities including women are underrepresented or discriminated, whether AI will definitely reinforce this discrimination.
Let me tell a story.
I don't tell you the result.
I am training the data looking at all the startups, including female founders and male funders in raising funds.
What the results show? The analysis results show that startups funded by women, they receive less funding, given other factors the same.
Women were discriminated in this capital market.
And this is like I started ox value and my colleague told me, you are selling against the wind in every aspect.
You you are women, you come from ethnic minority background and you are not young, you are not from STEM, et cetera.
So We see this result.
The coefficient for women is negative and significant.
What we decided is we leave out this gender variable in our algorithm, so we don't ask the founder their gender.
This becomes blind.
This negative coefficient this variable in fact was removed from our algorithm.
That is one way to reduce the systematic discrimination embedded in the dataset.
I think that's one example.
Also, we can see companies funded by women.
They are more responsible.
You see, the survival rate would be higher and we keep this Factor in the algorithm, which is a positive discrimination you see.
AI that also answers the question of whether we can train AI to be inclusive.
This is something that in the analysis when we see and our judgments can come in to train AI to be inclusive and remove those discriminate factors from the algorithm.
That's one example.
So There are ways to make the AI to be more inclusive and responsible, at least to be equal to men and women.
Another very interesting question is about stigma of using AI in learning and education.
Very good question, interesting because as a professor in the universities, we keep discussing this.
Of course, in Oxford is 900 years university, but it is the first university in the UK introduced guidance on using AI in teaching and using AI in research, give clear guidance.
Certain areas can be used and certain areas not allowed because we want to train our students key capabilities, key skills, which are the key skills for young people for your whole life.
Ability to learn.
AI cannot replace our learning capability.
We need to have this capability to learn, no matter technology, how well it goes, you see, and we have this learning capability.
Secondly, critical thinking.
AI doesn't have this critical thinking capability.
This is something for us to know Whatever answers created by AI or even by published papers, we know its strength, its limitations, when it works, when it doesn't work.
This critical thinking is also very important and so we distinguish, we know what is good.
These are the capabilities.
That's why teaching, we need to reform.
The teachers need to reform the way how we teach, what we teach, and even examinations, what kind of questions to answer, to ask what we want to see from students answer, what kind of capabilities, skills we want to see from the answers.
A lot of reforms the universities need to do, but I think the right approach, we know we need to equip our students with the skills to use AI to use AI to master AI.
Finally, I think AI, there are different skills.
In the society, including the question about labor force skills.
AI skills, I think also distinguish go deeper.
There are skills to use it and the skills to create it.
The skills to create AI and app may be more sophisticated, but the skills to use AI, we don't need to be frightened by AI.
The success for AI, they need to think about the user interface to be friendly to users.
To general users.
If AI is very difficult to use, that AI app wouldn't be successful.
It needs to be very friendly.
Like I studied a platform, which is a short video platform used a lot by farmers, migrants in the poor regions in China.
Why it's different from TikTok? Because it's user interface, the founder come from the poorest region of China.
He designed the user interface very friendly to people who is even illiterate.
So that user interface should be friendly, and therefore, that should reduce the barrier for people, you know, to use AI.
So that's a problem.
Digital literacy definitely is an area we need to address, but shouldn't be worried too much because it's too complicated AI, won't be successful, won't be popular.
You see.
So yeah, let's from me.
Yeah.
Thank you.
Thank you so much, no over to you.
Now, there were so many questions.
I wish I had an AI thing, which I could summarize, which I generally do.
That's where AI is making you a slave, which is a reality, but also very useful.
Just to respond to your question about the biasness of being lazy because somebody uses an AI, which is true, which happens with me as well because sometimes I also I'm not very comfortable in researching on something, so I put it on chat PT or Cloud or so many apps, and then it gives you some data.
Uh, so that's a reality and there will be so many stigmas attached to that aspect and we cannot run away.
People will keep calling you name by names and all those things, but you have to consider AI as something which is a technology.
Not something which is impacting your personality because same thing happened when Internet came, basically.
Most of us didn't have information, but through Internet, we started getting information.
But there were people who used to call them, you got this information through Internet anyway.
What about those people who don't have access to information and all those things? Same is when social media came.
We never realized that there will be a revolution of social media which is happening now.
We have to live with the technology, S is with the AI thing.
Second is, we are not training AI.
If you look at the broader aspect, AI is actually training us to train it.
Which is a reality.
We are more worried now that we have to put things into I and that would move away from biasness know.
Put something about one random biasness, AI will come back again with another biasness.
Technically, what's happening is vice versa.
AI is also training us indirectly.
That's one major fact which we need to take care.
We don't need to worry about people commenting on all those things.
It's a technology, we have to be ready and definitely it's going to stay forever.
That's one thing.
I don't think I need to respond to so many questions, but I will respond to your part on indigenous Same thing.
When we talk about movements or indigenous movement, caste movement, women movement, gender justice movement, it has to come through people who are from the community.
Unless and until you have people from the community, you won't be able to have that perspective into any application.
If you need to ensure that AI has all the perspective, it needs to include everyone and if you want to have that strong indigenous aspect and perspective within the AI, anything you're designing, you need to ensure that it is coming from the youth who are belonging to the indigenous group as well as other groups which are facing similar forms of discrimination.
But The strength, the power has to be in the hands of youth for decision making and other aspects because now AI often is being designed by the youth.
It's impacting the life of the youth and also it will keep impacting the life in the future of the world and the youth will be at the center of AI as well.
That has to be us only.
That's all.
Thank you very much.
Thank you so much.
We should wind up because we are already 5 minutes beyond time.
Best, would you like to share any thoughts before I try to summarize quickly? Thank you so much, ASG and thank you to our dear panelists and especially thank you to the youth representatives.
I think hearing your voices makes this conversation all the more richer and tells us here at the UN just how much work needs to be done, but not in isolation, but with you.
Because the questions you asked, I think embed also the answers because eventually it'll be you.
Who will need to find those answers or create better answers or help design those better answers.
We don't have all the answers, right? We have to work together and this new world, this brave new world we live in, this digital world creates so many questions that we all have to Ironically, we can't answer the answers that AI is bringing to us? We have to also solve them in our own capacity as human beings.
This I think is perhaps one of the biggest challenge us as human beings face now.
I think just to thank you all, and it has been a wonderful and very enriching conversation.
Thank you so much.
I must commend, first of all, our very distinguished panelists and all of you, I must say, whenever I go into rooms talking about AI, I find it both stimulating and sobering discussion.
Stimulates me with the opportunities, but it is very sobering too.
I want to flag three things for you to keep in mind.
And I could be wrong, probably, I don't think human beings have ever invented a technology which takes its own decisions and we're heading in that direction.
Nuclear weapons were devastating, but they could not be fired by themselves.
This is a big distinction and you might have heard they were trying to retire a model and model reacted.
It refused to leave that database.
This is the technology which has its own mind and the advancement Suan mentioned, there is a trajectory of AI, which is about advanced AI.
If we get there, that's very high risk.
This is unique about this technology, sobering stimulating.
Second point is, human beings can still take charge.
Let's not forget, remember cloning, cloning was a technology with a lot of promise, but human beings decided, no, we will not use it.
It's not good for us.
Human beings can still take charge on regulation, policy formulation, human capability, labor market governance.
All of these areas are in our hands and we should shape them, not the corporations.
That's where your role is critical to sensitize your governments.
Please play the role that you're supposed to play as public officials to make societies inclusive, equitable, and of course, progressive.
They should progress.
Al.
Lastly, keyword, I think we heard from all panelists, cooperation at all levels.
There are no solutions that you can pursue alone.
Please work with your communities, with your government, within your country and across countries through the United Nations and we are here.
UN is here, as ambassador mentioned, determined to make sure it serves humanity and it's not the other way around.
A round of applause for our panelists.
One for you also, for our participants, too.
Thank you so much.
And my sincere thanks to Abdesa team.
It brought us together and the team from the Mission of the Philippines.
Excellent work.
Thank you so much.