| APrIGF 2025 Session Proposal Submission Form | |||||||||||||
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| Part 1 - Lead Organizer | |||||||||||||
| Contact Person | |||||||||||||
| Ms. Megha Garg | |||||||||||||
| Organization / Affiliation (Please state "Individual" if appropriate) * | |||||||||||||
| Individual | |||||||||||||
| Designation | |||||||||||||
| Communication and Marketing Manager | |||||||||||||
| Gender | |||||||||||||
| Female | |||||||||||||
| Economy of Residence | |||||||||||||
| India | |||||||||||||
| Stakeholder Group | |||||||||||||
| Civil Society | |||||||||||||
| Part 2 - Session Proposal | |||||||||||||
| Session Title | |||||||||||||
| AI as Public Good: Analysing the interplay of Private Investments, Public Infrastructure and Public Accountability | |||||||||||||
| Thematic Track of Your Session | |||||||||||||
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| Description of Session Formats | |||||||||||||
| Workshop (60 minutes) | |||||||||||||
| Where do you plan to organize your session? | |||||||||||||
| Onsite at the venue (with online moderator for questions and comments from remote participants) | |||||||||||||
| Specific Issues for Discussion | |||||||||||||
| Governments worldwide are organising public service delivery ‘through and around digital technologies’ (Leonardi & Treem, 2020: 1602). The move towards this digitisation has also meant greater reliance on artificial intelligence over human decision-making systems (Carney, 2020; Gillingham, 2019). Countries are directly collaborating with the private sector to build public-facing AI infrastructure and directly supporting private-owned AI systems in public functions such as automated welfare delivery, and migration governance. Against this backdrop, the session will explore the emerging policy landscape on Artificial Intelligence across countries in Asia and the broader Global South. As governments begin to articulate their national positions on AI, we observe a growing divergence in how these frameworks are conceptualized and communicated—ranging from “missions” in India, to “strategies” in Indonesia and Vietnam, and “roadmaps” in the Philippines. Despite varying terminology, a shared momentum is visible across the region in actively shaping their own AI governance structures. The workshop will unpack: What national AI strategies/policies reveal about government priorities, values, and approaches. The implications of diverse framing (mission, roadmap, strategy) for national development, digital sovereignty, and state accountability. How these policies address (or overlook) critical concerns such as public access, data privacy, localization, and the equitable distribution of AI benefits. The session will bring together AI policy practitioners, researchers, and civil society actors from across Asia to: Assess the role of public and private actors in shaping AI infrastructures. Identify collective advocacy opportunities to ensure that AI policy development is for public interest and social justice. Through this collaborative exploration, the workshop aims to build regional solidarity and shared advocacy strategies. |
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| Describe the Relevance of Your Session to APrIGF | |||||||||||||
| Our session explores the interplay between public and private partnerships in the making of AI systems, policies that decide their deployment, and its implication on public services in the Global South. We believe this session aligns most strongly with the Access and Inclusion track. AI technologies and policies are increasingly being integrated into public service delivery, which should ideally reach every citizen. However, access to these services is not always equitable as many are left out due to gender, disability, caste, community, or lack of connectivity. This session examines how AI systems can bridge or widen these gaps, depending on how they are designed and governed. The Global South, in particular, faces a unique challenge: while AI and digital infrastructure hold great potential to close the digital divide, they also risk reinforcing existing inequalities if inclusion is not a core design principle. This makes accountability within public-private partnerships critically important. When services fail to reach marginalized communities—whether due to inaccessible design or embedded bias—who is held responsible? Through this discussion, we aim to evaluate how governments across Asia and other parts of the Global South are addressing inclusion and accountability in AI-driven public service systems. By examining national AI policies and the role of private actors in implementation, we hope to identify - issues and potential solutions for AI deployment and strategies for civil society and regional actors to advocate for better access and equitable participation This conversation builds on long-standing concerns around exclusion in digital systems—from early internet access to social media—and seeks to ensure that emerging AI systems do not repeat past mistakes. By anchoring our conversation in real-world policy examples and regional experiences, this session contributes to APrIGF’s goal of a multi-stakeholder approach to internet governanance |
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| Methodology / Agenda (Please add rows by clicking "+" on the right) | |||||||||||||
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| Moderators & Speakers Info (Please complete where possible) - (Required) | |||||||||||||
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| Please explain the rationale for choosing each of the above contributors to the session. | |||||||||||||
| We are a group of researchers who have explored public policy and AI from different perspectives and lenses. We set up a community of practice a few months ago to discuss a reimagination of AI as a public good. Our diverse experiences, backgrounds, and networks brought together unique synergies that has evolved each of our understanding. We now wish to host this workshop and invite stakeholders from the Global South to further expand our understanding. We are actively seeking to collaborate with individuals and organizations working at the intersection of gender, disability, and environment. | |||||||||||||
| If you need assistance to find a suitable speaker to contribute to your session, or an onsite facilitator for your online-only session, please specify your request with details of what you are looking for. | |||||||||||||
| The goal of our session is to ensure that the dialogue around technology governance moves beyond a singular lens and meaningfully includes diverse perspectives that are often overlooked. We are actively seeking to collaborate with individuals and organizations working at the intersection of gender, disability, and environment. Too often, equity and inclusion are addressed in conversations about emerging technologies only after harm has occurred—we aim to shift that dynamic. We would greatly appreciate your support in connecting us with suitable speakers for our session. |
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| Please declare if you have any potential conflict of interest with the Program Committee 2025. | |||||||||||||
| No | |||||||||||||
| Are you or other session contributors planning to apply for the APrIGF Fellowship Program 2025? | |||||||||||||
| No | |||||||||||||
| Upon evaluation by the Program Committee, your session proposal may only be selected under the condition that you will accept the suggestion of merging with another proposal with similar topics. Please state your preference below: | |||||||||||||
| Yes, I am willing to work with another session proposer on a suggested merger. | |||||||||||||
| Brief Summary of Your Session | |||||||||||||
| Our session on ‘AI as Public Good: Analysing the interplay of Private Investments, Public Infrastructure and Public Accountability’ sought to gather perspectives from across Asia on how governments across the world, and especially in Asia are conceptualising ‘AI for public good’. We focussed on private investments in public mechanisms to achieve welfare objectives in sectors like banking, healthcare, and education, and the accountability mechanisms or the lack thereof as Asian countries pursue this goal. Speakers presented their analysis of national strategies and policies in India, Singapore, Malaysia, and Indonesia that outline the state’s aspiration for AI diffusion and observed how attracting private investment and rebranding government efficiencies are two key motivations for states. Private investment to expand technological capabilities and government efficiencies from embedding AI and other data-driven technologies into public service delivery. The session opened for discussion on what the two motivations mean for public service delivery and developing an accountability framework. Sectorally, the speakers focussed on urban governance, healthcare, and financial inclusion. Speakers encouraged participants to respond on Mentimenter to gather perspectives on how these dynamics play out in their respective national contexts under the following themes: Balance between state funding and private capital and whether/how they align incentives with societal benefits. Ownership and control of data including accountability in case of misuse. Purpose and value in terms of the benefits they create in terms of achieving a public good. Reproduction of historical and demographic biases and national policy response to them. Possibility of citizen surveillance and the need for oversight structures. Participants highlighted regional challenges like limited public awareness and absence of robust redressal mechanisms. They stressed the need for adapting context-specific frameworks based on universal principles espoused by international organisations like the UN and the OECD. |
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| Substantive Summary of the Key Issues Raised and the Discussion | |||||||||||||
| Public and Private Investment The conversation began by looking at funding patterns in national AI ecosystems. Speakers outlined how public funding supports foundational research, data projects and pilots in some instances while private capital drives scale. Participants noted how this interplay/interdependence raises governance questions on the safeguards that need to be in place. They also pointed towards the lack of transparency in budgeting and performance evaluation which makes it difficult to track the public value it adds. Data Ownership and Governance Speakers showed evidence of emerging concerns around data ownership and governance in the context of public service delivery in sectors like healthcare, urban management, and education. Attendees discussed the role of public-private partnerships in smart city projects and how it blurs responsibility guardrails when citizen data from sensors, apps, platforms etc. are collected and aggregated in a centralized manner for purported efficiency, and concerns around control, access, and sharing of such data. Purpose and Value of AI Facilitators raised an urgent and recurring question in the context of public goods and data-driven delivery - what problems are governments actually trying to address? Participants noted that calls for efficient or modern public service delivery skip an important question of what social value it creates. Examples like the use of AI-based risk assessment in the financial system and how it fails to meet the objective of financial inclusion because of historical biases in data were discussed as examples. Bias, Equity, and Corrective Frameworks On a related note, participants also discussed the reasons and second order effects of biased data-driven systems and how bias gets replicated and amplified. Facilitators drew attention towards how algorithmic policing could label entire communities and localities as high-risk based and how welfare distribution could inadvertently exclude vulnerable groups as a consequence of biases in datasets. Surveillance and Public Accountability The final theme discussed the surveillance architecture that AI structures drive - especially in the context of facial recognition systems, predictive analytics in immigration departments. Participants raised concerns about how automated oversight could compromise privacy and autonomy. |
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| Conclusions and Suggestions of Way Forward | |||||||||||||
| Facilitators concluded the session with an open invite to participants to continue formal and informal engagement through an open document we shared with discussion notes and references. Attendees briefly discussed the need for regional-level knowledge sharing platforms to discuss how Asian countries are addressing the question of automated data-driven approaches to public good aspirations. There is a need to further explore governments’ approach towards implementing goals that are outlined in aspirational policy language through further research into the actual implementation of policy goals, and public procurement of AI to advance public good objectives. Participants highlighted regional challenges like limited public awareness and absence of robust redressal mechanisms and the need for adapting context-specific frameworks based on universal principles espoused by international organisations like the UN and the OECD. |
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| Number of Attendees (Please fill in numbers) | |||||||||||||
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| Gender Balance in Moderators/Speakers (Please fill in numbers) | |||||||||||||
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| How were gender perspectives, equality, inclusion or empowerment discussed? Please provide details and context. | |||||||||||||
| Equality and Inclusion were examined through the lens of public delivery systems that impact every individual—whether a citizen, immigrant, or refugee. The discussion emphasized the need to clearly define the boundaries between the use of AI within public delivery systems and its application in the private sector. Across all themes, the interplay between public and private roles was explored, particularly in relation to what it means for accountability, transparency, and ownership. | |||||||||||||
| Consent | |||||||||||||
 I agree that my data can be submitted to forms.for.asia and processed by APrIGF organizers for the program selection of APrIGF 2025. |
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