4 papers
"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
Lin Luo, Yuri Nakao, Mathieu Chollet +2
Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fair…
Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Tools
Lin Luo, Satwik Ghanta, Yuri Nakao +2
AI systems are increasingly used in high-stakes domains such as credit rating, where fairness concerns are critical. Existing fairness assessments are typically conducted by AI exp…
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
Lin Luo, Yuri Nakao, Mathieu Chollet +2
Numerous fairness metrics have been proposed and employed by artificial intelligence (AI) experts to quantitatively measure bias and define fairness in AI models. Recognizing the n…
Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI
Evdoxia Taka, Yuri Nakao, Ryosuke Sonoda +3
Fairness is a growing concern for high-risk decision-making using Artificial Intelligence (AI) but ensuring it through purely technical means is challenging: there is no universall…