mlsanityData to 7 Oct 2026

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

arXiv 2202.05338 ↗First posted 10 Feb 2022cs.CY cs.AI cs.LGcited by 47

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Abstract (arXiv)

In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. Nissenbaum [95] described four barriers to accountability that computerization presented, which we revisit in relation to the ascendance of data-driven algorithmic systems--i.e., machine learning or artificial intelligence--to uncover new challenges for accountability that these systems present. Nissenbaum's original paper grounded discussion of the barriers in moral philosophy; we bring this analysis together with recent scholarship on relational accountability frameworks and discuss how the barriers present difficulties for instantiating a unified moral, relational framework in practice for data-driven algorithmic systems. We conclude by discussing ways of weakening the barriers in order to do so.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 47 of its 179 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

47 of 179 references.

References · 47

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35 more
  1. 2014Certifying and removing disparate impactcited by 715
  2. 2017Exploring Generalization in Deep Learningcited by 595
  3. 2018Do Better ImageNet Models Transfer Better?cited by 560
  4. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  5. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
  6. 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
  7. 2020Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertaintycited by 128
  8. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 15; 2024: 10; 2025: 8; 2026: 6.

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  1. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  2. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
  3. 2024Visibility into AI Agentscited by 77
  4. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  5. 2023Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classificationcited by 31
  6. 2026The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systemscited by 27
  7. 2022From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible MLcited by 26
  8. 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Researchcited by 14
  9. 2024Acceptable Use Policies for Foundation Modelscited by 12
  10. 2023Going public: the role of public participation approaches in commercial AI labscited by 11
  11. 2022Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hexcited by 8
  12. 2024Fill In The Gaps: Model Calibration and Generalization with Synthetic Datacited by 8
35 more
  1. 2025Provocations from the Humanities for Generative AI Researchcited by 8
  2. 2022Non-Determinism and the Lawlessness of Machine Learning Codecited by 7
  3. 2024On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AIcited by 7
  4. 2022Adversarial Scrutiny of Evidentiary Statistical Softwarecited by 5
  5. 2023Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harmcited by 5
  6. 2023Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspectscited by 5
  7. 2022Quantitative AI Risk Assessments: Opportunities and Challengescited by 4
  8. 2025Trustworthy AI Must Account for Interactionscited by 4
  9. 2023Unravelling Responsibility for AIcited by 3
  10. 2024A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligencecited by 3
  11. 2022System Safety Engineering for Social and Ethical ML Risks: A Case Studycited by 2
  12. 2023Concrete Safety for ML Problems: System Safety for ML Development and Assessmentcited by 2
  13. 2023Human adaptation to adaptive machines converges to game-theoretic equilibriacited by 2
  14. 2023Machine Learning practices and infrastructurescited by 2
  15. 2023Moral Responsibility for AI Systemscited by 2
  16. 2023Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determinationcited by 2
  17. 2024Disentangled Representation Learning for Parametric Partial Differential Equationscited by 2
  18. 2025The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AIcited by 2
  19. 2026Long-Tail Knowledge in Large Language Models: Taxonomy, Mechanisms, Interventions and Implicationscited by 2
  20. 2023Datasheets for Machine Learning Sensorscited by 1
  21. 2023What's my role? Modelling responsibility for AI-based safety-critical systemscited by 1
  22. 2024From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gapcited by 1
  23. 2025Risks of AI-driven product development and strategies for their mitigationcited by 1
  24. 2025Enriching Moral Perspectives on AI: Concepts of Trust amongst Africanscited by 1
  25. 2022Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systemscited by 0
  26. 2023Survey of Trustworthy AI: A Meta Decision of AIcited by 0
  27. 2024The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing Risks and Benefits Through Practical Solutions and Use Casescited by 0
  28. 2024Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power Gridcited by 0
  29. 2025How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Languagecited by 0
  30. 2025Developing a Grounded View of AIcited by 0
  31. 2025Big AI is accelerating the metacrisis: What can we do?cited by 0
  32. 2026Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Educationcited by 0
  33. 2026Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AIcited by 0
  34. 2026How Formerly Incarcerated People Envision Technologies for Prison Parolecited by 0
  35. 2026Follow the Norm: Accounting for Fine-Tuning and Prompt Effects on Model Rationalescited by 0