Algorithmic Accountability

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Algorithmic accountability asks who is responsible when automated systems affect people, what evidence is needed to evaluate decisions, and how people can challenge or remedy harms. Approaches include audits and impact assessments, explanations and causal attribution, documentation and provenance, legal oversight and public-interest litigation, and mechanisms for contestation and redress. Researchers examine settings such as public services, hiring, advertising, and multi-agent AI, where responsibility may be spread across developers, vendors, deployers, and institutions. A central tension is that transparency and formal compliance can reveal system details without making decisions genuinely reviewable or providing effective remedies.

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33 papers3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

Beyond Detection: Governing GenAI in Academic Peer Review as a Sociotechnical Challenge
Mar 2, 2026Tatiana Chakravorti, Pranav Narayanan Venkit, Sourojit Ghosh +1Scholarly Peer Review AI Governance
Pennsylvania State University, University Park, Pennsylvania, USA · Salesforce AI Research, San Francisco, California, USA · University of Washington, Seattle, Washington, USA
fEDM+: A Risk-Based Fuzzy Ethical Decision Making Framework with Principle-Level Explainability and Pluralistic Validation
Feb 25, 2026Abeer Dyoub, Francesca A. LisiResponsible AI Moral Reasoning
Dept. of Informatics, University of Bari “Aldo Moro”, Bari, Italy · Centro Interdipartimentale di Logica e Applicazioni (CILA), University of Bari “Aldo Moro”, Bari, Italy
Retrofitters, pragmatists and activists: Public interest litigation for accountable automated decision-making
Nov 5, 2025Henry L Fraser, Zahra StardustAI Accountability Algorithmic Accountability
Lecturer, Queensland University of Technology, School of Law; Associate Investigator, Centre for Automated Decision-Making and Society · Senior Lecturer, Queensland University of Technology, School of Communication; Associate Investigator, Centre for Automated Decision-Making and Society

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Beyond Detection: Governing GenAI in Academic Peer Review as a Sociotechnical Challenge

Tatiana Chakravorti, Pranav Narayanan Venkit, Sourojit Ghosh, Sarah Rajtmajer

Pennsylvania State University, University Park, Pennsylvania, USA · Salesforce AI Research, San Francisco, California, USA · University of Washington, Seattle, Washington, USA

Scholarly Peer Review AI Governance Human Oversight of AI Automated Peer Review Algorithmic Accountability

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Generative AI tools are increasingly entering academic peer review workflows, raising questions about fairness, accountability, and the legitimacy of evaluative judgment. While these systems promise efficiency gains amid growing reviewer overload, their use introduces new sociotechnical risks. This paper presents a convergent mixed-method study combining discourse analysis of 448 social media posts with interviews with 14 area chairs and program chairs from leading AI and HCI conferences to examine how GenAI is discussed and experienced in peer review. Across both datasets, we find broad agreement that GenAI may be acceptable for limited supportive tasks, such as improving clarity or structuring feedback, but that core evaluative judgments, assessing novelty, contribution, and acceptance, should remain human responsibilities. At the same time, participants highlight concerns about epistemic harm, over-standardization, unclear responsibility, and adversarial risks such as prompt injection. User interviews reveal how structural strain and institutional policy ambiguity shift interpretive and enforcement burdens onto individual scholars, disproportionately affecting junior authors and reviewers. By triangulating public governance discourse with lived review practices, this work reframes AI mediated peer review as a sociotechnical governance challenge and offers recommendations for preserving accountability, trust, and meaningful human oversight. Overall, we argue that AI-assisted peer review is best governed not by blanket bans or detection alone, but by explicitly reserving evaluative judgment for humans while instituting enforceable, role-specific controls that preserve accountability. We conclude with role specific recommendations that formalize the support judgment boundary.

Latest papers

Beyond Detection: Governing GenAI in Academic Peer Review as a Sociotechnical Challenge

fEDM+: A Risk-Based Fuzzy Ethical Decision Making Framework with Principle-Level Explainability and Pluralistic Validation

Retrofitters, pragmatists and activists: Public interest litigation for accountable automated decision-making