Public Interest AI is an emerging innovation domain focused on developing, deploying, and governing artificial intelligence systems in service of the public good rather than narrow commercial or state interests. It encompasses the technical, policy, ethical, and participatory practices through which AI can advance human rights, democratic values, equity, transparency, and accountability. The domain integrates insights from computer science, law, political theory, social justice movements, and participatory design to reimagine how AI systems are developed and governed.
Public Interest AI emerged in response to growing concerns about algorithmic discrimination, surveillance, labor displacement, and concentration of AI power among a small number of large technology companies. It challenges the assumption that AI development is primarily a technical or commercial matter, insisting instead that it is fundamentally a governance challenge with implications for democracy, justice, and human flourishing. Key initiatives in this domain demonstrate that AI governance is not inevitable but rather a choice—systems can be designed to serve public purposes, communities can participate meaningfully in decisions affecting them, and AI development can be coordinated around shared ethical values rather than pure profit maximization.
The public interest AI domain brings together multiple constituencies: academic researchers studying algorithmic bias and fairness; civil society organizations advocating for algorithmic accountability; policymakers developing regulatory frameworks; open-source developers creating transparent alternatives to proprietary systems; government innovators deploying AI responsibly in public services; and affected communities insisting on participation in decisions affecting them. This coalition recognizes that effective AI governance requires interdisciplinary collaboration, transparency about how systems work, mechanisms for external oversight, meaningful community participation, and alignment with human rights and democratic principles.
Core to public interest AI is the recognition that technical solutions alone are insufficient—algorithmic fairness without governance is meaningless, and explainability without accountability becomes mere performance. Instead, the domain emphasizes integrated approaches combining technical transparency tools, policy mechanisms like impact assessments and regulations, institutional safeguards like ethics boards and public oversight, and participatory processes that give voice to communities affected by AI systems.