Reparative Algorithmic Impact Assessments A Human-Centered, Justice-Oriented Accountability Framework

While artificial intelligence (AI) promises transformative societal benefits, it also presents critical challenges in ensuring equitable access and gains for the Global Majority. These challenges stem in part from a systemic lack of Global Majority involvement throughout the AI lifecycle, resulting in AI-powered systems that often fail to account for diverse cultural norms, values, and social structures. Such misalignment can lead to inappropriate or even harmful applications when these systems are deployed in non-Western contexts. As AI increasingly shapes human experiences, we urgently need accountability frameworks that prioritize human well-being—particularly as defined by marginalized and minoritized populations.

Building on emerging research on algorithmic reparations, algorithmic impact assessments, and participatory AI governance, this policy paper introduces Reparative Algorithmic Impact Assessments (R-AIAs) as a solution. This novel framework combines robust accountability mechanisms with a reparative praxis to form a more culturally sensitive, justice-oriented, and human-centered methodology. By further incorporating decolonial, Intersectional principles, R-AIAs move beyond merely centering diverse perspectives and avoiding harm to actively redressing historical, structural, and systemic inequities. This includes colonial legacies and their algorithmic manifestations. Using the example of an AI-powered mental health chatbot in rural India, we explore concrete implementation strategies through which R-AIAs can achieve these objectives. This case study illustrates how thoughtful governance can, ultimately, empower affected communities and lead to human flourishing.