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

Abstract

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.

arXiv comment36 pages

References in corpus (4)

• Improving fairness in machine learning systems: What do industry practitioners need?
• 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
• Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
• Data Vision: Learning to See Through Algorithmic Abstraction

Cited by in corpus (21)

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• Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems
• A Critical Survey on Fairness Benefits of Explainable AI
• Broadening Privacy and Surveillance: Eliciting Interconnected Values with a Scenarios Workbook on Smart Home Cameras
• Normative Challenges of Risk Regulation of Artificial Intelligence and Automated Decision-Making
• Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"
• Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems
• Uncovering Bias in Personal Informatics
• On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model
• A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making
• Formalising Anti-Discrimination Law in Automated Decision Systems
• It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
• Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking
• Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software
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• Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems