IGF DC Webinar: AI for Good. AI for All
Internet Governance Forum Dynamic Coalitions - 17 July 2026
VIDEO | AUDIO | RECAP EN / ES / FR | ARCHIVE | PERMALINK
Host: Akshita Nyagi
Moderator: Dr. Rajendra Pratap Gupta - Chairman, IGF Dynamic Coalition on Digital Economy
Speakers: Markus Kummer - Chairman, IGF Support Association Executive Committee; Dr. Roman Chukov - Programme Management Officer & Dynamic Coalition Focal Point, IGF Secretariat; Kay Firth-Butterfield - Chief Executive Officer, Good Tech Advisory; Aashima Gupta - Global Director, Global Healthcare Solutions, Google Cloud; Eric Sutherland - Senior Health Economist, OECD; Dr. Jude Kong - Executive Director, Africa-Canada AI & Data Innovation Consortium (ACADIC); Christabel Randolph - Associate Project Director, Center for AI and Digital Policy (CAIDP); Aashna Uppal - PhD Candidate, Health Data Science, University of Oxford.
The webinar reflected on the major AI meetings held in Geneva during July 2026—including the Global AI Governance Dialogue, the AI for Good Global Summit, and the WSIS Forum—and considered how artificial intelligence can deliver broad public benefit while remaining ethical, inclusive, trustworthy, and human-centered. Across the discussion, panelists agreed that AI’s impact will depend less on technological capability than on governance, literacy, equitable access, and international cooperation.
Welcome and Introduction
Akshita Nyagi welcomed participants from around the world and framed the webinar around the central question of ensuring that AI serves both the public good and all people. She observed that AI is transforming healthcare, education, governance, business, and sustainable development, making it increasingly important that its development remains ethical, inclusive, and human-centric. She introduced each of the panelists before handing the discussion to the moderator.
Setting the Stage
Dr. Rajendra Pratap Gupta explained that the webinar was intentionally convened immediately after the series of international AI events in Geneva to reflect on what had been accomplished and to consider future directions for AI governance. He noted the momentum generated by the Global AI Governance Dialogue, the AI for Good Global Summit, and the WSIS Forum, and invited speakers to consider how these discussions should influence the next phase of AI development.
The Internet Governance Forum’s Dynamic Coalitions
Markus Kummer provided institutional context for participants unfamiliar with the Internet Governance Forum. He explained that the Dynamic Coalitions are independent, bottom-up, multistakeholder communities operating throughout the year rather than only during the annual IGF meeting. Although they operate under the broader UN umbrella, they remain independent bodies whose expert work contributes to the IGF’s intersessional activities and annual programme.
Geneva’s High-Level AI Discussions
Dr. Roman Chukov reviewed the unprecedented concentration of AI-related international meetings held in Geneva, highlighting the first UN Global AI Governance Dialogue alongside the AI for Good Summit and the WSIS Forum. He described the active participation of the IGF community throughout the week, previewed the annual IGF meeting in Nairobi in December 2026, and highlighted the Dynamic Coalition on Environment’s AI and Environment report as an important contribution that had subsequently been presented at the World AI Conference in Shanghai. He encouraged participants to become involved in one of the IGF’s twenty-three Dynamic Coalitions.
Governance, Literacy and Human Responsibility
Kay Firth-Butterfield argued that AI’s value depends fundamentally on governance and human responsibility. Drawing on more than a decade of work in AI ethics, she maintained that AI is neither inherently good nor bad but reflects the quality of its governance, the data on which it is trained, and the intentions of those deploying it.
She pointed to major opportunities in healthcare, legal systems, education, and dangerous occupations while also identifying significant risks arising from bias, misinformation, deepfakes, legal hallucinations, and poor AI literacy. She emphasized that billions of people remain absent from digital datasets, making genuine inclusion impossible without addressing global connectivity gaps. Throughout her remarks, she returned to the principle that AI remains a human tool whose outcomes ultimately depend upon human judgment, governance, and education.
Democratizing Expertise
Aashima Gupta presented AI as the next major technology platform after the Internet and mobile computing. Rather than simply connecting people to information, she argued, AI has the potential to democratize expertise itself.
Using healthcare as her primary example, she described how AI could improve diagnosis, expand access to trusted medical information, personalize education, broaden financial advice, and make specialist expertise available regardless of geography. She emphasized three priorities:
democratizing expertise
ensuring intentional inclusion
building trust through responsible AI
She argued that AI should become a planetary-scale technology, serving every country, every language, every developer, and every community. Trust, privacy, transparency, and security were not obstacles to innovation but prerequisites for achieving broad societal adoption.
Scaling AI Responsibly
Eric Sutherland examined AI through the lens of healthcare policy while drawing broader lessons applicable across sectors. He described three competing forces shaping AI deployment:
markets that seek rapid innovation
healthcare systems committed to doing no harm
public expectations of equitable access
He argued that previous waves of digital transformation focused too heavily on technology while neglecting people and organizational change. Successful AI deployment instead requires changing mindsets first, building appropriate skills second, and selecting technology only after those foundations are established.
Reflecting OECD work, he identified three essential requirements for scaling AI responsibly:
public trust and AI literacy
shared digital infrastructure and interoperability
sustainable economic models
He also noted that more than one-third of the public already consult AI before seeing healthcare professionals, making trustworthy AI guidance an increasingly urgent public policy issue.
AI Serving Communities
Dr. Jude Kong illustrated AI’s practical benefits through examples from African communities. Rather than beginning with technology, his projects identify local problems before designing AI solutions.
He described AI tools that assist maternal and child healthcare, improve diagnostic access in remote villages, and support educational opportunities where formal resources are scarce. These examples demonstrated how AI can strengthen communities that have historically lacked access to healthcare and specialist knowledge.
At the same time, he warned against developing AI that substitutes for human capacity rather than strengthening it. Referring to Geoffrey Hinton’s discussion of “maternal instinct,” he argued that AI systems should be designed to protect and empower humanity, not replace it. He also cautioned against allowing AI to erode creativity and independent thinking within education.
Human Rights and Public Interest
Christabel Randolph considered AI from the perspective of global AI governance and public policy. She argued that AI can either reduce or reinforce existing inequalities depending on how it is governed and deployed.
She called for three priorities:
investment in local regulatory and technical capacity
implementation of enforceable governance rather than relying solely on principles
evaluating AI according to who benefits and who bears its risks
She emphasized that AI governance should be grounded in democratic values, human rights, accountability, and the rule of law, noting that many international governance frameworks already exist. The remaining challenge is translating those principles into effective national implementation.
A Young Researcher’s Perspective
Aashna Uppal reflected on AI from the perspective of a health data science researcher. She described how AI is enabling research teams in Nigeria, Ethiopia, and Zambia to make meaningful use of valuable health datasets that previously existed only in paper form, improving research equity and strengthening local scientific capacity.
She argued that successful AI deployment requires solutions to be co-created with the communities they serve. She also raised concerns about AI’s environmental footprint, noting the growing energy demands of data centres and urging greater attention to sustainability so that AI’s benefits do not come at the expense of communities already experiencing the impacts of climate change.
Panel Discussion
During the moderated discussion, panelists examined several broader themes.
On AI as a public good, speakers generally agreed that AI itself is not inherently a public good. Rather, public benefit depends upon governance, shared infrastructure, public oversight, and equitable access. AlphaFold was highlighted as an example of AI accelerating scientific discovery by making protein structure predictions freely available to researchers worldwide.
On governance, panelists emphasized that:
governments must implement the international principles they have already endorsed
global cooperation should establish common frameworks rather than centralized control
organizations should adopt governance even where regulation remains incomplete
AI literacy should become a societal priority
Responding to audience questions, speakers also highlighted the importance of open-source technologies, interoperability, shared digital infrastructure, and affordable AI tools in enabling small businesses and underserved communities to benefit from AI innovation.
Closing Reflections
In their concluding remarks, speakers consistently emphasized that AI should augment rather than replace human expertise. Governance and innovation were presented as mutually reinforcing rather than conflicting objectives, while trust was identified as the key factor determining whether AI can achieve widespread adoption.
The panel agreed that substantial progress has already been made in establishing international AI governance principles. The next challenge is implementation through practical policies, stronger public institutions, improved AI literacy, and continued multistakeholder cooperation. Dr. Gupta concluded that the Internet Governance Forum’s multistakeholder model provides an appropriate forum for continuing that collaborative work as AI evolves.
RESOURCES
AlphaFold Protein Structure Database — the open database of 200M+ predicted protein structures Aashima Gupta cited as a Nobel Prize–winning public good
Google DeepMind — AlphaFold — background on the AI system that open-sourced protein structures for millions of researchers
OECD — Scaling Artificial Intelligence in Health — the report Eric Sutherland led on trust, foundations, and scaling AI in health
OECD AI Principles — the 2019 principles Eric Sutherland and Christabel Randolph referenced as a governance foundation
Center for AI and Digital Policy (CAIDP) — Christabel Randolph’s organization tracking AI policies across 90+ countries
Africa-Canada AI & Data Innovation Consortium (ACADIC) — Dr. Jude Kong’s network of 23 countries building community-driven AI
Fast Forward — the tech-nonprofit accelerator Kay Firth-Butterfield highlighted for funding AI-for-good startups
HDR UK / Turing Wellcome PhD Programme — the Oxford Big Data Institute doctoral programme Aashna Uppal is part of
UNESCO Recommendation on the Ethics of AI — the ethics framework endorsed by 193 countries, cited by Christabel Randolph
Council of Europe Framework Convention on AI — the first legally binding AI treaty, referenced on red lines and ratification
IGF Dynamic Coalitions — the 23 coalitions Roman Chukov invited participants to join