Thematic analysis of 43 AI contestation cases, using Bovens's relational accountability model, produces categories of demands from below, institutional pushback, outcomes, and contextual factors shaping effective contestation.
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
The study proposes the Gradual Voluntary Participation (GVP) framework to reconceptualize participatory AI governance in journalism as a gradual and voluntary process using a bidimensional matrix.
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
Participatory AI approaches in forced displacement settings risk 'participation washing' due to entrenched power dynamics between aid recipients, providers, donors, and host nations, requiring independent governance structures.
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.
citing papers explorer
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Push and Pushback in Contesting AI: Demands for and Resistance to Accountability
Thematic analysis of 43 AI contestation cases, using Bovens's relational accountability model, produces categories of demands from below, institutional pushback, outcomes, and contextual factors shaping effective contestation.
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Gradual Voluntary Participation: A Framework for Participatory AI Governance in Journalism
The study proposes the Gradual Voluntary Participation (GVP) framework to reconceptualize participatory AI governance in journalism as a gradual and voluntary process using a bidimensional matrix.
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The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
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From experimentation to engagement: on the paradox of participatory AI and power in contexts of forced displacement and humanitarian crises
Participatory AI approaches in forced displacement settings risk 'participation washing' due to entrenched power dynamics between aid recipients, providers, donors, and host nations, requiring independent governance structures.
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Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.