Responsible AI for Shared Prosperity
20 Feb 2026 16:00h - 17:00h
Responsible AI for Shared Prosperity
Summary
The panel, chaired by UK Deputy Prime Minister David Lammy, examined how AI can be harnessed for development in Africa and Asia through language-focused initiatives and new computing infrastructure [1-3][6-8][9-11]. Lammy outlined the AI for Development programme, which includes expanding AI into more than 40 African languages, establishing Africa’s first public-sector AI compute cluster at the University of Cape Town, and launching an Asia AI for Development Observatory in partnership with Canada, Germany, Japan, Sweden and the GSMA Foundation [1-3][6-8][9-11].
Kenyan envoy Philip Thigo emphasized that the Global South possesses intelligence but has historically lacked the power to record and transmit it, making representation of oral cultures in AI models a matter of civilizational survival [23-33][37-44]. He argued that building research capacity, talent, and context-specific language models is essential for sovereignty and for delivering concrete use cases across the continent’s 2,000 languages [39-42][43-45]. The Masakane African Language Hub, described by its chair, aims to impact one billion Africans by developing high-quality data, inclusive benchmarks, gender-responsive projects such as Project Echo, and sustainable community-led AI ecosystems [52-60][61-70][71-77].
Indian CEO Shekhar Sivasubramanian explained that Wadwani AI designs applications in 14-16 languages from the outset, ensuring rural-urban inclusivity and tangible utility in health, education and agriculture [84-92][94-102]. He gave examples of a multilingual disease-surveillance system that alerts governments every four hours and an oral-reading tool that provides real-time feedback to children and teachers, illustrating how language and purpose are inseparable for adoption [95-102][108-113].
German parliamentary state secretary Babel Kofler highlighted that AI can only overcome inequality if it is inclusive, noting that bias in data and neglect of dialects must be addressed and that Germany has contributed through the Fair Forward initiative and partnerships with India to collect multilingual datasets [135-143][145-151]. The UK, Canadian, Japanese governments, Microsoft and the Gates Foundation are jointly funding public-good projects such as the African Compute Initiative-a high-performance GPU cluster at UCT-and the Lingua Africa open-core platform to turn language data into deployable services [258-270][272-279][224-233]. Microsoft’s Natasha Crampton stressed that compute is the enabler for making AI linguistically and culturally aware, required both for model training and for testing with local speakers, and that trustworthy AI depends on adequate infrastructure [224-233][236-244][245-248].
Across the discussion, participants agreed that without affordable compute and representative language resources, African and Indian researchers cannot contribute to or shape global AI systems [39-42][224-233][258-270]. They also concurred that public-sector investment and multi-stakeholder collaborations are necessary to fill market gaps, sustain talent and ensure that AI benefits are equitably distributed [212-214][258-270][71-77]. The session concluded that coordinated, inclusive AI development-grounded in local languages, robust compute and shared governance-offers a pathway to an equitable AI future for the Global South [13-14][126-127].
Keypoints
Major discussion points
– Launching and scaling AI initiatives that prioritize African (and Asian) languages and compute capacity.
David Lammy outlined the AI for Development programme, the Masakane African Languages Hub, a public-sector AI compute cluster at the University of Cape Town, and the Lingua Africa open-core partnership - all aimed at bringing AI to over 40 African languages and providing the hardware needed for local model training [1-8][9-13][160-176][224-233][260-270].
– Ensuring cultural representation and linguistic sovereignty.
Philip Thigo emphasized that the Global South’s oral heritage is at risk if AI models ignore local languages, and that building data, talent and research capacity is essential for “sovereignty” [22-33][38-45]. The Masakane Chair added that the hub’s four-pillar approach (data, research, innovation, sustainability) seeks to capture the nuance of each language and to preserve cultural memory [51-60][61-70][71-76].
– Concrete, impact-driven use cases across sectors.
Wadwani AI described multilingual health-surveillance, disease-outbreak alerts, and an oral-reading-fluency tool for children, illustrating how language-aware AI can deliver tangible benefits in health, education and agriculture [84-102]. Masakane’s Project Echo was highlighted as a gender-responsive initiative that uses African-language AI to empower women’s economic participation and health [71-74].
– Multi-stakeholder partnerships and public-goods funding to fill market gaps.
Representatives from the UK, Canada, Germany, the Gates Foundation, Microsoft and IDRC stressed that commercial markets overlook low-resource languages, so coordinated public-sector and philanthropic investment is required to create open data, benchmarks and compute resources [8-10][126-134][145-151][183-214][224-236][257-279].
– Compute infrastructure as the critical enabler and a barrier to entry.
Both the African Compute Initiative and Microsoft’s commentary highlighted the exponential cost disparity for high-performance GPUs in Africa, arguing that dedicated clusters are indispensable for training, testing and deploying culturally-aware models [3][5][224-233][260-270][274-279].
Overall purpose / goal of the discussion
The panel was convened to announce and explain a coordinated, multi-nation effort to make AI inclusive of Africa’s (and Asia’s) linguistic diversity, to build the necessary data and compute foundations, and to demonstrate how such infrastructure can be turned into real-world applications that advance health, education, gender equity and economic development.
Overall tone and its evolution
The conversation began with an optimistic, visionary tone-celebrating “brilliant, genuinely African-led initiatives” and the promise of an equitable AI future [2][13]. As speakers detailed the challenges of language extinction, talent scarcity, and compute cost, the tone shifted to a more urgent, problem-focused stance, emphasizing the existential risk of exclusion [30-33][224-233]. Throughout, the dialogue remained collaborative and constructive, ending on a hopeful, call-to-action note that highlighted partnership, public-goods investment and the potential for lasting impact [214-219][254-259].
Speakers
– Co-Moderator – Panel moderator (role: co-moderating the discussion).
– David Lammy – Deputy Prime Minister of the United Kingdom; MP; leads the UK’s AI for Development programme. [S4]
– Natasha Crampton – Chief Responsible AI Officer, Microsoft; expertise in AI ethics, trustworthy and multilingual AI. [S6]
– Ankur Vora – Chief Strategy Officer and President, Africa and India Office, Gates Foundation; focuses on philanthropic strategy for AI-driven development. [S9]
– Chenai Chair – Director, Masakane African Language Hub; specialist in African language NLP, data collection, and AI model benchmarking for low-resource languages. [S11]
– Shekar Sivasubramanian – CEO, Wadwani AI; Chennai Chair and Director of the Mazakani African Languages Hub; works on applied AI solutions for health, education, agriculture and multilingual technology in India.
– Julie Delahanty – President, International Development Research Centre (IDRC), Canada; expertise in research funding for AI in low- and middle-income countries. [S14]
– Philip Thigo – His Excellency Ambassador Philip Thigo, Special Technology Envoy of the Government of Kenya; focuses on AI policy, technology strategy and representation of African languages in AI. [S18]
– Barbel Kofler – Parliamentary State Secretary to the Federal Minister for Economic Cooperation and Development, Germany; works on international development policy and AI governance. [S20]
Additional speakers:
– Debra Kofler – Mentioned during the panel change-over; no further role or expertise detailed in the transcript.
Opening Remarks – David Lammy
UK Deputy Prime Minister David Lammy opened the session by outlining the AI for Development programme. The programme has three pillars: (i) extending AI services to more than 40 African languages, (ii) creating Africa’s first public-sector AI compute cluster at the University of Cape Town, and (iii) launching an Asia AI for Development Observatory [1-8][9-13]. He noted the partnership network – the UK, Canada’s IDRC, the Gates Foundation, Germany, Japan, Sweden and the GSMA Foundation [1-8][9-13]. Lammy announced funding for four start-ups, including Torn AI in Morocco, which is building a voice-interface for low-literacy rural users to access digital and financial services [280-281]. He framed the work as a moral crossroads: AI can either concentrate power and widen inequality, or act as a force for good that uplifts humanity [13-14].
Panel Introduction – Co-Moderator
The co-moderator introduced the panel: Philip Digo, Kenya’s Special Technology Envoy; Babel Kofler, Germany’s Parliamentary State Secretary; Shekhar Sivasubramanian, CEO of Wadwani AI and Chair of the Masakane African Languages Hub; and Julie Delahanty, President of IDRC [15-21].
Philip Digo’s Response
When asked how AI in local languages could shape Kenya’s digital development (the continent has roughly 2 000 languages) [20-21], Digo described an “age of intelligence” in which AI reshapes how people live, learn and work [22-27]. He argued that the Global South has never lacked intelligence, but has lacked the power to record, transmit and recognise it, especially because many cultures are oral [28-33]. He warned that the absence of African languages in current models threatens an existential loss of civilisation [32-33]. Digo highlighted the need for research capacity, talent pipelines and a full AI stack-from data collection to model training-to achieve linguistic sovereignty and deliver context-specific use cases [38-45]. He cited youth in Kenya using “Chagipiti” (a local reference to ChatGPT) to create culturally relevant content [284].
Masakane African Languages Hub – Chair
The Chair explained that the Masakane African Languages Hub emerged in 2019 from a community-driven effort to digitise local languages when no external funding was available [51-55]. Its ambition is to reach 1 billion Africans through 50 of the most spoken languages, delivering economic, health and social benefits while preserving linguistic evolution [54-55][56-60]. The hub operates on four pillars:
1. Data – expanding high-quality, diverse datasets (building on the JW300 Bible corpus) [61-64];
2. Research & Benchmarking – creating an African speech-and-text benchmark to capture local nuances [65-67];
3. Innovation – allocating 40 % of the budget to concrete use-cases, notably Project Echo, a gender-responsive intervention that improves women’s economic empowerment and health in African languages [71-74];
4. Sustainability – focusing on institutional capacity-building so that open-source models can spawn local businesses and ensure long-term African-led AI [75-77].
Wadwani AI – Shekhar Sivasubramanian
Sivasubramanian described Wadwani AI’s inclusive design principle: every solution is built for at least 14-16 languages and must bridge the rural-urban divide [84-92]. He showcased two flagship projects: a multilingual disease-surveillance system that scans Indian news every four hours in 16 languages and alerts the government to outbreaks [94-99]; and an oral-reading-fluency tool that records children’s spoken reading, provides instant feedback and helps teachers tailor instruction [100-102]. He stressed that language and utility are inseparable-a model must deliver tangible value to achieve adoption [108-113].
Babel Kofler – Germany
Kofler reinforced the ethical dimension, stating that AI can only be a game-changer for the Sustainable Development Goals if it is inclusive and built on bias-free data [135-143]. She highlighted the importance of dialectal variation, noting that ignoring it reproduces cultural marginalisation. Kofler cited Germany’s Fair Forward initiative (launched in 2019) that collaborates with India to collect multilingual datasets for citizen-facing services [282-283][145-151].
Lingua Africa Announcement – Masakane Chair
The Chair announced Lingua Africa, an open-core, community-governed language-infrastructure platform funded by a multi-million-pound partnership among the UK, the Gates Foundation, Microsoft AI for Good and Masakane [160-176][170-178]. The platform will coordinate community-governed language infrastructure, domain-specific data collection, model development and deployment pathways.
Ankur Vora – Gates Foundation
Vora framed language as a public good that markets have abandoned because commercial incentives focus on English and Mandarin [183-190][191-215]. He argued that when markets are broken, coordinated public-good funding from governments, foundations and tech firms is required to develop low-resource language AI, and he reaffirmed support for Lingua Africa [188-192][205-212][160-176].
Natasha Crampton – Microsoft
Crampton highlighted that compute is the enabler for language-aware, culturally-sensitive AI. High-performance GPUs are needed not only to fine-tune models with locally collected data but also to test them with native speakers and to run day-to-day services [224-233][232-247]. She warned that AI diffusion is currently twice as fast in the Global North, underscoring the urgency of closing the compute gap [225-227].
Julie Delahanty – IDRC
Delahanty detailed the African Compute Initiative, the first dedicated high-performance GPU cluster for public institutions in Africa, to be hosted at the University of Cape Town [258-271]. The cluster will provide modern GPUs, fast storage and networking, enabling African researchers to train large models, test innovations quickly and support projects such as the Masakane Hub [274-279][272-273]. She also noted community-driven localisation work, citing subtitles created by the Amara.org community [285].
Consolidated Consensus
All speakers agreed that:
* Compute infrastructure is foundational for building and deploying multilingual AI models [3-4][224-233][258-271][39-40][188-192];
* Linguistic inclusion is essential to prevent cultural extinction and to deliver equitable AI benefits [13][28-33][54-66][86-92];
* Public-good, multi-donor partnerships are needed to fill market failures for low-resource languages [8-10][126-134][145-151][183-215];
* Domain-specific use cases in health, education and agriculture demonstrate real-world impact [10-11][71-74][94-102]; and
* Capacity-building in talent, research and institutions underpins AI sovereignty [40-41][64-66][86-92][267-271][212-215].
Points of Divergence
1. Funding Model – Vora stressed that markets are broken and only public-good investment can address language gaps [188-192][205-212]; Lammy called for strong state intervention to avoid leaving AI to the marketplace [218-219]; Sivasubramanian highlighted the role of private-sector risk-taking and innovation [84-86].
2. Priority: Compute vs. Data – Crampton and Delahanty positioned compute as the immediate bottleneck [224-233][267-271]; Kofler and the Masakane Chair emphasised high-quality, bias-free data and benchmarks as the prerequisite [136-143][60-66][70-76].
3. Framing of Language Preservation – Digo framed it as an existential civilisational threat [28-33]; Vora described it as a market failure requiring public-good investment [188-192][205-212].
Key Take-aways
* Linguistic inclusion safeguards cultural heritage and ensures AI benefits all communities [13][28-33][54-66][86-92].
* The Masakane Hub targets 50 major African languages, aiming to impact 1 billion people through data expansion, benchmarking, gender-responsive projects and sustainability [54-55][61-70][71-77].
* Effective AI solutions must be multilingual and directly useful, as shown by disease-surveillance, oral-reading tools, and voice-interface start-ups [10-11][94-102][280-281].
* Bias-free, representative data and local testing are prerequisites for trustworthy AI [135-143][232-247].
* Market forces ignore low-resource languages; coordinated public-good funding is required [188-192][205-215].
* Compute capacity is a major bottleneck; the African Compute Initiative will provide the first public-sector high-performance GPU cluster in Africa [258-271][274-279].
* Multi-stakeholder partnerships (UK, Gates, Microsoft, IDRC, Germany, Canada, GSMA) are mobilised to fund language infrastructure, compute and applied projects [8-10][126-134][145-151][183-215][224-233][258-271].
* Governance, ethics, gender-responsive design and long-term sustainability are central to ensuring AI remains safe, inclusive and equitable [71-77][135-143][224-233].
Resolutions & Action Items
* Launch of Lingua Africa, an open-core, community-governed language-infrastructure platform funded by a UK-Gates-Microsoft partnership [160-176][170-178].
* Allocation of substantial funding to Masakane for data collection, benchmark creation, use-case development and sustainability activities [9-10][71-77].
* Support for four additional start-ups (including Torn AI) via the GSMA Foundation to deliver responsible AI for underserved populations [9-10][280-281].
* Establishment of the African Compute Initiative – a dedicated high-performance GPU cluster at UCT for public-sector researchers [258-271][272-273].
* Commitment of 40 % of Masakane’s budget to concrete use cases, notably Project Echo for women’s economic empowerment and health [71-74].
* Development of an African speech-and-text benchmark to evaluate models in local contexts [66-67].
* Ongoing capacity-building programmes to train African researchers, data scientists and AI engineers [40-41][64-66][86-92][212-215].
Open Questions & Unresolved Issues
* Scaling beyond 50 languages – a roadmap is needed to extend support to the full 2 000 + African language ecosystem [42-45].
* Long-term financing – mechanisms to sustain the Masakane hub and the compute cluster after the initial grant period remain to be defined.
* Governance of Lingua Africa – the precise community-governed decision-making structure and benefit-sharing model require clarification.
* Data ownership and privacy – strategies for collecting high-quality data for extremely low-resource dialects while protecting community rights are still under discussion.
* Deployment at scale – methods to roll out voice-interface solutions like Torn AI to low-literacy, rural users across diverse contexts need further elaboration.
* Impact measurement – robust metrics for evaluating gender-responsive interventions (e.g., Project Echo) and health outcomes from AI tools are required [71-74].
* Balancing public-good and private-sector roles – ensuring that private innovators benefit from public infrastructure without compromising local sovereignty [218-219][84-86].
* Benchmarking and scenario-aware evaluation – defining standards for accuracy, cultural relevance and context-specific performance of African language models [66-67][232-247].
Alignment with Policy Context
The discussion mirrors recent AI-readiness reports that call for “the most useful” AI in Africa rather than the most powerful [S38], and stress that digital sovereignty must incorporate linguistic dimensions [S41][S86]. The emphasis on public-sector compute as critical national infrastructure aligns with recommendations to treat AI infrastructure as a public good [S55][S103]. By combining government funding, multilateral development partners and private-sector expertise, the panel’s agenda directly addresses identified gaps in capacity, infrastructure and inclusive governance [S39][S40][S52].
Overall, the panel presented a coordinated, multi-nation effort to build the data, talent and compute foundations required for multilingual, culturally-aware AI, while recognising the ethical imperative to preserve linguistic heritage and to deliver equitable development outcomes across Africa and Asia.
to make AI work in more than 40 African languages. This is a brilliant, genuinely African -led initiative which helps people to access AI in the languages that they actually use in their everyday lives. Second, we’re investing in Africa’s first dedicated public sector AI computer cluster at the University of Cape Town. Too many African researchers are held back by costs and a lack of access. And the hope is that this new hub will give them the computing power to build and train models locally. And third, we’re launching the Asia AI for Development Observatory. This is a new network to support research, responsible AI governance, to protect rights and to ensure AI reflects the realities of people’s lives across the region.
All of these initiatives are effectively part of our AI for Development programme, launched when we hosted the first of these AI summits back at Bletchley Park three years ago and made in partnership with Canada’s International Development Research Centre and as part of a wider collaboration to coordinate investments with the Gates Foundation, the governments of Germany, Japan and Sweden, as well as Community Jamil. And as part of our partnership with the GSMA Foundation, we’re proud to announce the support to four additional start -ups and these innovative businesses will harness responsible AI and will be able to support the development of AI for the future. to support the needs of underserved people. across Asia and Africa. They include Torn AI in Morocco, which creates voice interfaces to local dialects to help low -literacy rural users access digital and financial services through simple spoken interactions.
And all of these initiatives will make a real difference to people across the continent of Africa and Asia. But I hope they’ll do a bit more than that. Yesterday, I spoke about the choice the world faces, the two paths before us, one which sees AI take power and opportunity away from people and sadly divides us, and one that sees AI used as a force for good to solve problems and uplift all of humanity. and the projects I’ve mentioned, the ones we’re going to hear about today and the many new institutions and coalitions that are now emerging can help make sure we go down the right path and that is a path of a safe AI, an inclusive AI and importantly an equitable AI for everyone.
So let’s turn now to our panel, an exceptional group of leaders from across India and Africa and I’m going to get an introduction to our panel members and then I’ll start with the first question.
Thank you. It’s my pleasure to introduce, joining our Deputy Prime Minister on the stage, we have His Excellency Ambassador Philip Digo, Special Technology Envoy of the Government of Kenya. We have Dr. Babel Kofler. parliamentary state secretary to the federal minister for economic cooperation and development of Germany. We have Shekhar Sivasubramanian, CEO of Wadwani AI and Chennai Chair, Director of the Mazakani African Languages Hub. And so to the first question to Philip. So we’re beginning to see an attempt well we all experience how large language models are affecting our lives on a daily basis. I certainly use a, I don’t use chat GPT but I use a secure network which isn’t taking my stuff because obviously for obvious reasons as Deputy Prime Minister of the UK I have to be a bit careful but I’m using it to research and you know usually really get quickly to a that I don’t fully understand.
So as we move this on to local languages, dialects, and across Africa we’ve got an estimated 2 ,000 languages, how do you see AI in local languages shaping the next phase of your country’s digital development having been to Kenya many times and knowing the many groups that are there, how does this really work on the ground?
Thank you so much, Right Honourable Prime Minister. I think this moment is so profound that I don’t think you guys are realising what is happening here. I think the first thing is to understand that we’re actually in the age of intelligence, right? So it’s not about ICTs or technology, it’s about our AI shape. I think how we live, learn, work, collaborate. and engage. From our point of view, it’s a civilizational discussion. The Global South has never lacked intelligence, as we know, right? So what it has lacked is the power to define how that intelligence is recognized, recorded, or transmitted. Because our entire culture’s values have been coined in language. The Global South is largely an oral civilization.
And so the current models lacking our language means our civilization are at risk, almost existential, to be extinct. And I think this initiative, in our view, begins to ensure that we are represented in the current age of intelligence, but also our intelligence is part of our global collective history and memory. And so I think which means then how we engage in this is that now with the capabilities like Masakani, it means that when I get into Chagipiti that you refused to mention, is that… Young people in Kenya, who are the number one users of Chagipiti? by the way, are not only seeking emotional advice or guidance from these models, but then when they engage with these models, it actually also represents their cultures and civilization.
I think for me that’s how it works practically on the ground. First of all, it’s representation and existence. The second part, of course, is it also works when we have the entire stack. And you mentioned a couple of things around finding the models, but I think it would be interesting to see how we find the compute, the talent that then influences, develops the data, develops the language. The research and development capability, which I was in the first instance, and that was an amazing initiative because then we need to build research capacity and capability because talent development is the first instance of sovereignty. Then the final point, of course, is the specific use cases and languages, especially in the African context.
Again, you say 2000. So each of the 2000 are very context -specific. Kiswahili and Yoruba are not the same, and neither are their applications in the context of the African context. sense in even our history and cultures in Africa. So I think that capability, as diverse as it could be, also ensures that our diversity in the African continent is represented in the future models.
And that’s wonderful. And the first point you made is really about sort of seeing yourself in this story that the global community is going to be telling in relation to intelligence. And we know that in the past, Africa has been written out of that story. So it’s hugely important that African languages intellect history over thousands of years is in this storybook. So should I tell us then how the Masa Kani African languages is addressed? And how are we addressing these issues and really working as a tangible, real thing?
Thank you so much. Minister. So I think I want to say that I am proud to be representing the Masakane community that started in 2019, wanting to see their own languages represented in the global domain, and they did it by the bootstraps. No one wanted to fund them, and they came together and said, hey, how do I ensure that the language that I speak is captured digitally? So the Masakane African Language Hub emerges from that community -driven initiative, where our main goal is to impact 1 billion Africans through 50 of the most spoken languages with relevant AI tools that will allow for economic growth, health, and social benefit, and also working towards the preservation and also capturing the evolution of African languages.
The 2 ,000 -plus are growing, and so then even as Honorable Philip Degas mentioned, the diversity of the language is growing. So I think it’s a very important thing to do. Thank you. Thank you. Like I speak Shona, but the Shona I speak in Harare is not the same as the Shona spoken in Mutai. So it’s really capturing that diversity and nuance of that work. So what we do with support from the funding collaborative and the partners that we have is actually think about enabling the ecosystem through partnerships and grant making. So we specifically focus on four pillars of work, which is around data. So expanding and diversifying high quality data. In 2019, there wasn’t as much data, but the Masakana community actually started building up that data based from the JW300 Bible data set that had been created.
Secondly, we’re also looking at research. So it’s important for us to take it on as an ecosystem invention, where we are looking at developing and refining inclusive AI machine learning models, but also thinking about the tooling that’s resourcing these. So what we are working on specifically, that is having a benchmark project where we’re actually… going to create a relevant African benchmark looking at speech and text because the current benchmark models out there do not nuance the realities on the African context. And then also looking at innovation. So again, a lot of the questions that we’ve seen is when you create the data, where does it go? Is it taken up in the market? What’s the impact?
And so for us, we’re actually working 40 % of the funding that we have will go to creating use cases and impacting use cases. And one special mention I want to put forward is actually that we are working on a project called Project Echo, which means enhancing communications for her opportunities. This is a gender responsive intervention that exists in the context of high gendered inequality on the continent. And what that does is it will provide relevant use cases in African languages that lead to impact on women’s economic empowerment and health. And that’s a significant part of us recognizing the context we exist in. And then lastly, we really are thinking about sustainability. and right now we’re in a moment where there is resourcing, where there is funding and we also come from a moment where people were doing it without funding.
So we’re thinking about institutional capacity building for the African NLP community which will actually then see businesses coming up from these open source models, people innovating off the data that’s created and sustainability beyond the Masakana community which has been happening right now but then this funding allows us to actually have African -led AI which is built for impact. Thank you very much.
Thank you very much. Centres Africa but also importantly the fundamental inequality and gender issues that sit at the heart not just of Africa but making sure that women are, a big part of this story. Shekhar, bringing India into this and thinking of Wadwani AI and we’re sitting here in the most populous country on the planet. There are also lots of languages and tremendous diversity but also innovation and range across this country. So tell us how Wadwani AI is working at the heart of that innovation here in India. Thank you. First, the
work we do is applied AI, which means we solve for problems in health, education and agriculture. And we’ve been doing it for the last seven years. The moment you work in India, the very first design principle that you start with is the ability to be… inclusive and embrace the entire population. So the dimensions of population we are looking at are language. So you start with at least 14 to 16 languages. You don’t even think of an application otherwise. Second, you also think of complete inclusivity, which means you need to think through the divide between rural and urban, the kinds of applications that will be delivered to people which will be of use to them. Third, are applications fundamentally must be useful to people.
Then they open out their ability to learn languages, changes that better interface with technology that can actually be of use to them. So that utility value sits at the heart of everything that we do, which drives a lot of behavior both by us as well as the ecosystem. Just as an example, we do media disease surveillance. We’ve been doing it for a while, which picks up every article published in India, it runs four hours, and it picks. Events of interest in health. 16 languages and it’s been running for the last two and a half to three years. It uses AI and it tells you in this region this many people got this disease at this time.
It runs every four hours and it tells the central government if it’s a disease outbreak what should you do. Another completely different example. We collect data from children in a couple of states and that will expand to 14 to 16 states where we have the largest data set of spoken local language. And which again in of itself of no use but when you provide something called oral reading fluency which assists the poorest child to read a paragraph and the AI tells you what you read well, what you did not read well and assists the teacher to cohortize the students and provide them information. Suddenly the language and the application you cannot distinguish between them. It is very important in human contexts to provide some value to the person in any interchange.
If you can work the value then the adoption is possible. option is easy. If you divorce the two, people don’t understand why I’m doing what I’m doing. It looks like an encumbrance. So for us, at the heart of our innovation is what does it mean for the person. Independent of which, we do analysis on various languages. We’ve done one on Tibetan, where we’ve preserved their entire culture by taking, we worked in Dharmashala, as well as in Karnataka. We digitized their entire library system and allowed the communities there to gain employment using it. Likewise, we plan to work on multiple less -used languages, Pan -India. We believe, it’s our position that, and we’ve got Agrivani, Healthvani, everything that we do is multilingual.
Everything that we do collects data. We have the largest data sets now, incidentally, of the work we do. It’s not what we do. It comes as a by -product of the work that we do. Over a period of time, it is my heart. Considered an humble opinion that these models using AI will take time. We should be ready to write this for a period of time. We should be ready to invest in deep research and or very utilitarian based approaches so that you can take the community along with you. That is super important. The theory is interesting, the practice is different. There is a theory as to how to design roads in India. I will keep quiet after a bit.
Very
very good example. Obviously I talked about the UK as a donor country doing this in partnership with others, Canada, Sweden, but also the German government and we’re joined by Babel Koffler just to bring the donor a perspective really to this and why this is so important. Thank
you very much, Deputy Prime Minister. Thank you. Oh, there’s no one? What did I do? Oh, thank you. Thank you very much. I should put it on first, that’s true, yeah. Thank you very much, Deputy Prime Minister. I wouldn’t talk if it’s coming to AI in a manner of donor and recipient, because at the end of the day I think it’s a new technology where we all have to bridge if we really want to make it useful for everybody. And that’s also our interest, of course, from the German side. We see that AI can only be really the game changer to overpower… …to overcome inequality, to fulfill the promises of the SDG. And that’s for every country important, not only for global laws.
global South that’s important for everybody, can only be that game changer if it is inclusive. That starts with data at the end of the day and how biased data is. And if you talk about bias in data, language is quite close to it. You were pointing out how important it is and how you differently speak in various variations of your language. I really understand that I don’t speak standard German normally, so I also use a dialect, and that’s quite different from Hamburg. So we all have something to include also, which is connected with a cultural momentum, and we see so many languages neglected, totally neglected, dialects, cultures, because it’s not only the language, it’s what the language is transporting also, which is neglected.
And that’s why we really try to be part. And we are very proud to be part of your initiative. also. We were starting in 2019 with discussing those topics, working on an initiative called Fair Forward. That’s part of the initiative. And working also with partner countries like India on collecting data sets, so really to collect the necessary data on those local languages, which at the end of the day is offering then or should offer service to citizens in their mother tongue in multilingual countries or contexts, for example. So for us, it’s of utmost importance. Happy to be part of the initiative. We want to stay a reliable partner on that, and we will be part of that initiative.
And I hope the idea is spreading and growing. Thank you. Thank
you. We’ll now have a small… Changeover in our panellists. If I could ask, if we could have another big round of applause, please, for His Excellency Philip Higo, Debra Kofler and Shekhar Sivasubramanian. And now joining us on stage, we will have Ankara Vora, Chief Strategy Officer and President of the Africa and India Office at the Gates Foundation. Julie Delahunty, President of Canada’s International Development Research Centre. And Natasha Crampton, Chief Responsible AI Officer at Microsoft. Thank you very much.
Back to Chennai, my understanding is that Masakami are announcing a new multi -million pound partner for an open call today for Lingua Africa. So can you tell us a little bit more about this initiative and the gap that it’s designed to close effectively? And then why this moment is so important for African languages as a whole and AI?
Thank you so much Deputy Prime Minister. So yes, I do have the honour with my esteemed panellists to actually announce Lingua Africa. So with Masakami, which I’ve said means to build together, we’ve been working with researchers and communities across the continent to close the gap in how African languages are being used. And how African languages are represented in the AI systems. What we’ve constantly seen is that… I think I did mention this, is that it’s not just about data. It’s about whether the language resources actually translate into tools people can use, particularly in healthcare, education, agriculture, and public services, because those are the developmental domains that we’re likely to have significant impact with. So together with Microsoft AI for Good and the Gates Foundation, as well as our AI for D partners, LINGUA Africa will be a multi -partner open core focused on open community -governed language infrastructure, which will directly enable real -world AI applications.
I think a lot of the times as we’re developing AI solutions, the question becomes, if we’re building them in a lab, will they work in the real world? And that’s also consistently part of what we’re doing with the benchmarking work. So how we’ll do this is actually then it’ll be a use case or impact -focused specific approach. Where we will do model development, we will collect targeted data in those… specific domains and then also support strong pathways for deployment and adoption. So this is us working with multiple entities, the academic community, our partners here on stage with us, but also the tech entrepreneurs who are actually building up these solutions. And then for us, it’s quite simple.
The goal is to make sure that language is not a barrier anymore into including people into these solutions, particularly if you think about digital public infrastructure interventions. They need to be in languages that people communicate with because you will leave behind a majority of people if they are in languages that they do not understand. So that is our most significant contribution right now. Thank you.
Thank you. And obviously, we’re very pleased in the UK to be partnering with the Gates Foundation on new support for linguistic diversity across AI. But just explain, Ankur, the role. that the hub has effectively in that wider impact on the global south.
This is on? It’s on, all right. Languages matter. Can you first join me in giving a big round of applause to Chennai for this amazing movement. It is kind of brilliant where we are in this moment in time. Let me talk about three whys. One is why care about language? The second is why care about investing in language? And the third one is why care about investing in initiatives like Masakane? The first one, I think so everybody knows, but it’s useful to repeat it. And many people have talked about this before. Because we want to make… We want to make sure that the power of AI… actually changes lives. History is not going to remember us for the models we developed or the speeches we give here.
History is going to remember the impact we all had. We’re talking about mothers and babies not dying. We’re talking about the next generation growing up in a world without infectious diseases. We’re talking about hundreds of millions of people escaping the clutches of poverty. Those kind of things matter. And the solutions are there. They can get better. But we need to find a way of these solutions getting translated for these use cases. So that’s why we need to care about this thing. Why invest in language? Because the markets are broken. The markets are broken. Private sector companies are investing in models developed in English and Mandarin. And it makes sense for the markets to do that.
Because that’s where the economics work. But just because… the economics don’t work for the small resource languages doesn’t mean that we shouldn’t be investing in this. And that’s the point why all of us need to get together and say we need to do something about it. When markets are broken, funders can get together and invest in public goods. And that’s what we all are doing right now. UK government, Canadian government, the Japanese, Microsoft, IDRC, everybody is getting together and making the point that we need to invest in public goods because these markets are broken and this is an important thing. And so I’m quite excited about the fact that we’re all sitting at this panel, people in this audience, and saying we’re going to make sure that as we think about tomorrow, the important thing of developing solutions in the right languages that can solve the problem.
Thank you. be a problem that we will tackle. Thank you.
Thank you. I’ve got to say as a politician I like the idea that the state intervenes and doesn’t just leave it to the marketplace to determine where to put the funds, I’ve got to say. But obviously we are, and you have to in terms of the innovation, work at the cutting edge and that cutting edge is most often in the private sector as well, taking those risks to develop and innovate and here Microsoft and cloud computing is hugely important, Natasha, and it’s important to understand that interaction between the cloud. I think that the Masakani hub as it has been described so well, by Shaniai and how important it is that computation and cloud technology helps the innovation of these local languages.
Could you say a little bit more about that? But there is another subset to this, which is getting the balance right so that the languages and often the communities that we’re supporting that are at the front line have equity in this and don’t lose their own sovereign capabilities, which has been a theme of this conference. So I wonder if you could just reflect on that as well.
is an urgent priority. Our own analysis shows that we have AI diffusing in the global north at roughly double the rate that we have it diffusing in the global south at the current time, and that is exactly why we need partnerships like these in order to start to put the right infrastructure in place to close that gap. Now, language is particularly important in terms of overcoming that AI divide. As we’ve heard many speakers say today, nobody is going to use AI if it does not speak the language that you speak, and importantly, that it does not work in the context, in the specific scenario in which you need to use it. So language -aware and scenario -aware, AI.
AI is incredibly important to empowering people to put… the technology to work in the use cases that mean the most to them. And that’s why we’re so thrilled to be partnering with Masakane, as well as the Gates Foundation, and the UK government on this Lingua Africa initiative. So how does compute come into all of this? I think, quite simply, compute is the enabler of making language and culturally aware AI. It’s a critical component of it. So when we take a base model that may have just been trained, like most models, on data sets that are predominantly English, we need to make sure that we can do this responsibly, locally -led data collection that Shania was talking about earlier.
And then we need to do some further work on the models to essentially ingest that data and make it well -registered. And that’s what we’re trying to do. And that’s what we’re trying to do. And that’s what we’re trying to do. And that’s what we’re trying to do. And that’s what we’re trying to do. And that’s what we’re trying to do. And that’s what we’re trying to do. And that’s what we’re trying to do. And that’s what we’re trying to do. takes compute. Then it’s very important once we’ve actually made the model language, linguistically and culturally aware, we need to make sure that we’re testing it with local language speakers and in the right scenarios.
That also, that testing, that also takes compute. And then finally, the day -to -day use of this technology, it also requires computing power. So we’re really here today as an enabler of an Africa -led effort by Africans for Africans to create this linguistically aware and multi -culturally aware technology, and compute fundamentally is just the enabler of it. I think my last thought to offer here today is I think these types of initiatives just really reinforce that trustworthy AI is not going to be the best tool for computing. I think it’s going to be the best tool for computing. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. because of the choices that we make, the ways in which we choose to build and test and deploy these AI systems and for us at Microsoft it’s really important that we do take all of those steps to represent the world as it is multicultural, multilingual and deeply interconnected so we’re thrilled to be part of this initiative.
Thank you very much.
And Julie, so we very much described this journey that we’ve been on since Bletchley Park and talked about languages, talked about computing. In a sense, AI, that’s the foundation stone for communities but just to round off this event, how do we see the opportunities going forward? particularly, where do we need to get to?
Thank you. Thanks, everybody, for being here and for the Deputy Prime Minister for welcoming us. We’re incredibly proud at IDRC to be part of the AI4D initiative with the UK government and to be partnering with the Masa Kani African Language Hub as well as the African Compute Initiative. And I think going back a little bit to the Microsoft views on it, I think it’s very similar for us. Researchers in lower and middle -income countries really have to have strong computing power to be able to do the kind of cutting -edge AI work that Shana and others are doing. But right now, of course, they do face a lot of barriers. We did a study that showed the incredible increased cost of getting compute capacity, the difference between getting it in Germany and getting it in the UK.
But in an African country, the costs are exponentially larger. So that computing cost and how much it is, the local infrastructure that might be limited, the GPUs that are the… hardware that’s really driving the powers modern AI is also very difficult and hard to access for African countries. So it really makes it difficult for them to fully participate in global AI innovations. The African Compute Initiative is going to change all that, we hope. It is going to be the first dedicated high -performance computing cluster for public institutions in Africa. It will be based in South Africa at the University of Cape Town. And that initiative is going to include modern GPUs. It’s going to have faster and better storage capacity and much faster networking.
And it’s that kind of computing power that is essential. It’s essential for training large AI models. It’s essential for testing new ideas more quickly, as you mentioned. Subtitles by the Amara .org community And it’s been essential and will be essential for things like the Masakani African Language Hub. Both the initiatives that I’m talking about are really responding to the foundational gaps. So whether that’s compute capacity or the kinds of representative and robust data sets, both of those things are absolutely necessary. And if you don’t have those foundations, then you can’t contribute to AI systems. And if you can’t contribute to AI systems, then you can’t shape the AI systems. So it’s absolutely critical for Africa’s AI innovations to have those foundational elements that exist.
And I think the lessons that we’re going to learn through a lot of this programming is going to help other regions and other lower resource contexts to do that kind of work. And in terms of the next steps or the things that we can do with that, I mean, you can imagine some of the obvious things. I mean, some people have already mentioned it, but things like having…
Thank you very much. Thank you very much. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you.
“UK Deputy Prime Minister David Lammy opened the session.”
The knowledge base lists David Lammy as the Deputy Prime Minister of the United Kingdom, confirming his role in opening the session [S1].
“The continent has roughly 2 000 languages.”
The knowledge base notes that there are over 2,000 documented African languages, corroborating the figure cited in the report [S106].
“Babel Kofler is Germany’s Parliamentary State Secretary.”
Source [S21] identifies Bärbel Kofler as the Parliamentary State Secretary to the Federal Ministry of Economic Cooperation and Development, confirming her title.
“Philip Digo is Kenya’s Special Technology Envoy.”
The knowledge base records the envoy’s name as Philip Thigo, not Philip Digo, indicating a naming error in the report [S17].
“The partnership network includes the Gates Foundation among other donors.”
Source [S109] mentions collaboration with partners such as the Gates Foundation in AI-related initiatives, providing additional context to the reported partnership list.
The panel demonstrates strong convergence on four pillars: (1) the necessity of compute infrastructure; (2) the centrality of linguistic inclusion; (3) the need for collaborative public‑good funding; (4) the importance of real‑world, domain‑specific applications; and (5) capacity building for sustainable AI sovereignty.
High consensus – most speakers echo each other’s points, indicating broad political and technical agreement that coordinated investment in compute, data, talent, and multilingual use cases is essential to achieve inclusive, equitable AI for the Global South.
The panel shows strong consensus on the importance of multilingual, inclusive AI for Africa and Asia, but diverges on how to fund, prioritize, and implement it—whether through public‑good investment versus private‑sector innovation, whether to focus first on compute infrastructure or on data quality, and how to frame the urgency of language preservation. These disagreements are moderate rather than fundamental, reflecting different strategic emphases rather than outright conflict.
Moderate disagreement: strategic and priority differences that could affect coordination and resource allocation, but not undermining the shared goal of equitable AI development.
The discussion was shaped by a series of pivotal remarks that moved it from a generic announcement of funding to a nuanced debate about cultural survival, market failure, gender equity, and technical infrastructure. Ambassador Thigo’s existential framing forced the panel to treat language inclusion as a civilizational imperative. The Masakane Hub’s four‑pillar strategy and Wadwani AI’s concrete multilingual applications grounded that imperative in actionable projects. Vora’s market‑failure analysis justified the coalition of public‑good funders, while Crampton’s emphasis on compute linked policy and cultural goals to the necessary technical backbone. Together, these comments created a coherent narrative: inclusive AI requires data, talent, compute, and sustained public investment, and without them, entire cultures risk erasure. The dialogue therefore progressed from high‑level ideals to concrete, interdisciplinary solutions, highlighting the interdependence of language, infrastructure, and equitable policy.
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.
Related event
