Push and Pushback in Contesting AI: Demands for and Resistance to Accountability

2026-05-10

AI Summary

This study addresses the underexplored dynamics of how affected communities pursue accountability following harms caused by artificial intelligence and how responsible parties respond. Drawing on Bovens’ relational accountability model, the research conducts a thematic analysis of 43 real-world cases to develop the first empirical typology of AI accountability contestation. It uncovers bottom-up advocacy strategies, institutional response patterns, outcome categories, and their underlying determinants. The findings not only illuminate the diverse tactics employed by accountable actors to evade responsibility but also distill actionable mechanisms for both contestation and institutional response. By doing so, this work offers scholars, policymakers, and advocates a practical framework to advance effective AI accountability in practice.

Abstract

As AI becomes increasingly embedded in daily life, it has been shown to fail critically, cause harm, and spark public controversy, prompting affected communities, workers, and public-interest groups to contest it. Yet how these contestations unfold in practice remains underexplored. We address this gap by developing an empirically grounded account of AI contestation dynamics. We do so through a thematic analysis of 43 real-world cases in which affected actors direct demands toward those responsible for AI development and deployment, seeking redress, influence, or changes to AI practices. Situating our work within Bovens's relational model of accountability, we conceptualize contestation as accountability-seeking: a dynamic, iterative process in which actors "from below" direct explicit demands at actors "from above," who respond by accepting, resisting, or circumventing accountability. Our analysis produces empirically grounded categories of contestation strategies, institutional response tactics, outcome types, and the contextual factors that shape them, illuminating how accountability is pursued and evaded in practice. We show that those being contested often deploy a range of strategies to limit their accountability. Based on these insights, we offer guidance for researchers, policymakers, advocates, and other stakeholders seeking to support effective AI contestation, with particular attention to anticipating and countering institutional strategies used to evade accountability.

Problem

• AI contestation
• accountability
• institutional resistance
• public controversy
• algorithmic harm

Innovation

• AI contestation
• accountability
• relational accountability
• institutional resistance
• empirical case analysis