2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
"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…
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…