Reflexive Design for Fairness and Other Human Values in Formal Models
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Abstract (arXiv)
Algorithms and other formal models purportedly incorporating human values like fairness have grown increasingly popular in computer science. In response to sociotechnical challenges in the use of these models, designers and researchers have taken widely divergent positions on how formal models incorporating aspects of human values should be used: encouraging their use, moving away from them, or ignoring the normative consequences altogether. In this paper, we seek to resolve these divergent positions by identifying the main conceptual limits of formal modeling, and develop four reflexive values--value fidelity, appropriate accuracy, value legibility, and value contestation--vital for incorporating human values adequately into formal models. We then provide a brief methodology for reflexively designing formal models incorporating human values.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 2; 2025: 1.
11 papers.
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
- 2022Mimetic Models: Ethical Implications of AI that Acts Like Youcited by 11
- 2024Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasetscited by 11
- 2022Towards a Roadmap on Software Engineering for Responsible AIcited by 9
- 2022It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Gamescited by 3
- 2023Grasping AI: experiential exercises for designerscited by 3
- 2023Designing Data: Proactive Data Collection and Iteration for Machine Learningcited by 2
- 2023Machine Learning practices and infrastructurescited by 2
- 2021Building Bridges: Generative Artworks to Explore AI Ethicscited by 1
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualismcited by 1
- 2025Prototyping Digital Social Spaces through Metaphor-Driven Design: Translating Spatial Concepts into an Interactive Social Simulationcited by 0
- 2025Prototyping Digital Social Spaces through Metaphor-Driven Design: Translating Spatial Concepts into an Interactive Social Simulation3 Oct 2025
- 2024Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets18 Nov 2024
- 2024Intuitions of Compromise: Utilitarianism vs. Contractualism7 Oct 2024
- 2023Grasping AI: experiential exercises for designers2 Oct 2023
- 2023Machine Learning practices and infrastructures13 Jul 2023
- 2023Designing Data: Proactive Data Collection and Iteration for Machine Learning24 Jan 2023
- 2022It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games12 Sep 2022
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering12 Sep 2022
- 2022Mimetic Models: Ethical Implications of AI that Acts Like You19 Jul 2022
- 2022Towards a Roadmap on Software Engineering for Responsible AI9 Mar 2022
- 2021Building Bridges: Generative Artworks to Explore AI Ethics25 Jun 2021