Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Tools

Ähnliche Einträge

• EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
  von: Luo, Lin, et al.
  Veröffentlicht: (2024)
• "I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
  von: Luo, Lin, et al.
  Veröffentlicht: (2025)
• Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI
  von: Taka, Evdoxia, et al.
  Veröffentlicht: (2023)
• Towards Multi-Stakeholder Evaluation of ML Models: A Crowdsourcing Study on Metric Preferences in Job-matching System
  von: Yokota, Takuya, et al.
  Veröffentlicht: (2025)
• Which Demographic Features Are Relevant for Individual Fairness Evaluation of U.S. Recidivism Risk Assessment Tools?
  von: Nguyen, Tin Trung, et al.
  Veröffentlicht: (2025)