mlsanityData to 7 Oct 2026

What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design

arXiv 2007.06718 ↗First posted 13 Jul 2020cs.CY cs.HC cs.LGcited by 14

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Abstract (arXiv)

Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholder preferences to create algorithmic systems that account for those stakeholders' values. Using algorithmic student assignment as a case study, we argue that optimizing for individual preference satisfaction in the distribution of limited resources may actually inhibit progress towards social and distributive justice. Individual preferences can be a useful signal but should be expanded to support more expressive and inclusive forms of democratic participation.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jul 2020.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 4; 2024: 2; 2025: 0; 2026: 1.

14 papers.

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  1. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
  2. 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30
  3. 2023Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Servicescited by 29
  4. 2021Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"cited by 26
  5. 2022Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approachescited by 23
  6. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  7. 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
  8. 2022Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholderscited by 15
  9. 2021Beyond "Fairness:" Structural (In)justice Lenses on AI for Educationcited by 12
  10. 2023Going public: the role of public participation approaches in commercial AI labscited by 11
  11. 2022Human-Centric Research for NLP: Towards a Definition and Guiding Questionscited by 9
  12. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
2 more
  1. 2024Beyond Predictive Algorithms in Child Welfarecited by 0
  2. 2026Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governancecited by 0