5 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…
TofuML: A Spatio-Physical Interactive Machine Learning Device for Interactive Exploration of Machine Learning for Novices
Wataru Kawabe, Hiroto Fukuda, Akihisa Shitara +2
We introduce TofuML, an interactive system designed to make machine learning (ML) concepts more accessible and engaging for non-expert users. Unlike conventional GUI-based systems,…
Accountability of Generative AI: Exploring a Precautionary Approach for "Artificially Created Nature"
Yuri Nakao
The rapid development of generative artificial intelligence (AI) technologies raises concerns about the accountability of sociotechnical systems. Current generative AI systems rely…
Towards Multi-Stakeholder Evaluation of ML Models: A Crowdsourcing Study on Metric Preferences in Job-matching System
Takuya Yokota, Yuri Nakao
While machine learning (ML) technology affects diverse stakeholders, there is no one-size-fits-all metric to evaluate the quality of outputs, including performance and fairness. Us…