Toward a decision process of the best machine learning model for multi-stakeholders: a crowdsourcing survey method. - Takuya Yokota; Yuri Nakao (UMAP (Adjunct Publication), 2022)

Stakeholder-in-the-Loop Fair Decisions: A Framework to Design Decision Support Systems in Public and Private Organizations. - Yuri Nakao; Takuya Yokota (HCI (28), 2023)

Exploring the Impact of Lay User Feedback for Improving AI Fairness. - Evdoxia Taka; Yuri Nakao; Ryosuke Sonoda; Takuya Yokota; Lin Luo; Simone Stumpf (CoRR, 2023)

Towards Multi-stakeholder Evaluation of ML Models: A Crowdsourcing Study on Metric Preferences in Job-Matching System. - Takuya Yokota; Yuri Nakao (CHIRA (1), 2024)

The Need for User-centred Assessment of AI Fairness and Correctness. - Simone Stumpf; Evdoxia Taka; Yuri Nakao; Lin Luo; Ryosuke Sonoda; Takuya Yokota (UMAP (Adjunct Publication), 2024)