mlsanityData to 7 Oct 2026

This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology

arXiv 1909.11869 ↗First posted 26 Sep 2019cs.CY cs.HCcited by 24

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Abstract (arXiv)

The explosion in the use of software in important sociotechnical systems has renewed focus on the study of the way technical constructs reflect policies, norms, and human values. This effort requires the engagement of scholars and practitioners from many disciplines. And yet, these disciplines often conceptualize the operative values very differently while referring to them using the same vocabulary. The resulting conflation of ideas confuses discussions about values in technology at disciplinary boundaries. In the service of improving this situation, this paper examines the value of shared vocabularies, analytics, and other tools that facilitate conversations about values in light of these disciplinary specific conceptualizations, the role such tools play in furthering research and practice, outlines different conceptions of "fairness" deployed in discussions about computer systems, and provides an analytic tool for interdisciplinary discussions and collaborations around the concept of fairness. We use a case study of risk assessments in criminal justice applications to both motivate our effort--describing how conflation of different concepts under the banner of "fairness" led to unproductive confusion--and illustrate the value of the fairness analytic by demonstrating how the rigorous analysis it enables can assist in identifying key areas of theoretical, political, and practical misunderstanding or disagreement, and where desired support alignment or collaboration in the absence of consensus.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 7; 2023: 7; 2024: 3; 2025: 0; 2026: 3.

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  1. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  2. 2022Detecting Shortcut Learning for Fair Medical AI using Shortcut Testingcited by 52
  3. 2021Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLPcited by 47
  4. 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkitscited by 33
  5. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  6. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  7. 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
  8. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  9. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  10. 2022Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systemscited by 14
  11. 2021Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systemscited by 9
  12. 2023Dynamic fairness-aware recommendation through multi-agent social choicecited by 9
12 more
  1. 2024The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisationcited by 7
  2. 2023On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Modelcited by 6
  3. 2023Uncovering Bias in Personal Informaticscited by 3
  4. 2026Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Riskscited by 3
  5. 2022System Safety Engineering for Social and Ethical ML Risks: A Case Studycited by 2
  6. 2023Concrete Safety for ML Problems: System Safety for ML Development and Assessmentcited by 2
  7. 2023The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Developmentcited by 2
  8. 2024Formalising Anti-Discrimination Law in Automated Decision Systemscited by 2
  9. 2022A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Makingcited by 1
  10. 2024Mapping the Potential of Explainable AI for Fairness Along the AI Lifecyclecited by 1
  11. 2026Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AIcited by 1
  12. 2026Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Softwarecited by 0