Requirements for Explainable Smart Systems in the Enterprises from Users and Society Based on FAT. - Yuri Nakao; Junichi Shigezumi; Hikaru Yokono; Takuya Takagi (IUI Workshops, 2019)

Generation of Hints to Overcome Difficulty in Operating Interactive Recommender Systems. - Yuri Nakao; Takuya Ohwa; Kotaro Ohori (IntRS@RecSys, 2019)

Investigating audio data visualization for interactive sound recognition. - Tatsuya Ishibashi; Yuri Nakao; Yusuke Sugano (IUI, 2020)

Use of Machine Learning by Non-Expert DHH People: Technological Understanding and Sound Perception. - Yuri Nakao; Yusuke Sugano (NordiCHI, 2020)

One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification. - Kenji Kobayashi; Yuri Nakao (CoRR, 2020)

Design Methods for Artificial Intelligence Fairness and Transparency. - Simone Stumpf; Lorenzo Strappelli; Subeida Ahmed; Yuri Nakao; Aisha Naseer; Giulia Del Gamba; Daniele Regoli (IUI Workshops, 2021)

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)

Towards Involving End-users in Interactive Human-in-the-loop AI Fairness. - Yuri Nakao; Simone Stumpf; Subeida Ahmed; Aisha Naseer; Lorenzo Strappelli (CoRR, 2022)

Toward Involving End-users in Interactive Human-in-the-loop AI Fairness. - Yuri Nakao; Simone Stumpf; Subeida Ahmed; Aisha Naseer; Lorenzo Strappelli (ACM Trans. Interact. Intell. Syst., 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)

Technical Understanding from IML Hands-on Experience: A Study through a Public Event for Science Museum Visitors. - Wataru Kawabe; Yuri Nakao; Akihisa Shitara; Yusuke Sugano (CoRR, 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 Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness. - Yuri Nakao; Lorenzo Strappelli; Simone Stumpf; Aisha Naseer; Daniele Regoli; Giulia Del Gamba (Int. J. Hum. Comput. Interact., 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)

What Should Be Considered to Support well-being with AI: Considerations Based on Responsible Research and Innovation. - Yuri Nakao (CoRR, 2024)

Technical Understanding from Interactive Machine Learning Experience: a Study Through a Public Event for Science Museum Visitors. - Wataru Kawabe; Yuri Nakao; Akihisa Shitara; Yusuke Sugano (Interact. Comput., 2024)

Reflexive co-design: A participatory design protocol for socially responsible information technology based on responsible research and innovation. - Yuri Nakao (Int. J. Hum. Comput. Stud., 2025)

EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders. - Lin Luo; Yuri Nakao; Mathieu Chollet; Hiroya Inakoshi; Simone Stumpf (Proc. ACM Hum. Comput. Interact., 2025)

Accountability of Generative AI: Exploring a Precautionary Approach for "Artificially Created Nature". - Yuri Nakao (CoRR, 2025)

TofuML: A Spatio-Physical Interactive Machine Learning Device for Interactive Exploration of Machine Learning for Novices. - Wataru Kawabe; Hiroto Fukuda; Akihisa Shitara; Yuri Nakao; Yusuke Sugano (CoRR, 2025)