Lin Luo
Research title: Interactive machine learning fairness and explanations
Publications
2026
Luo, Lin ORCID: https://orcid.org/0000-0002-0310-3158, Nakao, Yuri, Chollet, Mathieu
ORCID: https://orcid.org/0000-0001-9858-6844, Inakoshi, Hiroya and Stumpf, Simone
ORCID: https://orcid.org/0000-0001-6482-1973
(2026)
"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment.
In: ACM CHI Conference on Human Factors in Computing Systems (CHI 2026), Barcelona, Spain, 13–17 April 2026,
(Accepted for Publication)
Di Campli San Vito, P. et al. (2026) Empowering Stakeholders with Participatory Auditing of Predictive AI: Perspectives from End-Users and Decision Subjects without AI Expertise. In: CHI' 2026, Barcelona, Spain, 13–17 April 2026, (Accepted for Publication)
2025
Luo, Lin ORCID: https://orcid.org/0000-0002-0310-3158, Nakao, Yuri, Chollet, Mathieu
ORCID: https://orcid.org/0000-0001-9858-6844, Inakoshi, Hiroya and Stumpf, Simone
ORCID: https://orcid.org/0000-0001-6482-1973
(2025)
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders.
In: 28th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW), Bergen, Norway, 18-22 Oct 2025,
(doi: 10.1145/3710908)
2024
Stumpf, Simone ORCID: https://orcid.org/0000-0001-6482-1973, Taka, Evdoxia
ORCID: https://orcid.org/0000-0001-7011-3367, Nakao, Yuri, Luo, Lin
ORCID: https://orcid.org/0000-0002-0310-3158, Sonoda, Ryosuke and Yokota, Takuya
(2024)
The Need for User-centred Assessment of AI Fairness and Correctness.
In: UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization, Cagliari, Italy, 01-04 Jul 2024,
pp. 523-527.
ISBN 9798400704666
(doi: 10.1145/3631700.3664912)
Conference Proceedings
Luo, Lin ORCID: https://orcid.org/0000-0002-0310-3158, Nakao, Yuri, Chollet, Mathieu
ORCID: https://orcid.org/0000-0001-9858-6844, Inakoshi, Hiroya and Stumpf, Simone
ORCID: https://orcid.org/0000-0001-6482-1973
(2026)
"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment.
In: ACM CHI Conference on Human Factors in Computing Systems (CHI 2026), Barcelona, Spain, 13–17 April 2026,
(Accepted for Publication)
Di Campli San Vito, P. et al. (2026) Empowering Stakeholders with Participatory Auditing of Predictive AI: Perspectives from End-Users and Decision Subjects without AI Expertise. In: CHI' 2026, Barcelona, Spain, 13–17 April 2026, (Accepted for Publication)
Luo, Lin ORCID: https://orcid.org/0000-0002-0310-3158, Nakao, Yuri, Chollet, Mathieu
ORCID: https://orcid.org/0000-0001-9858-6844, Inakoshi, Hiroya and Stumpf, Simone
ORCID: https://orcid.org/0000-0001-6482-1973
(2025)
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders.
In: 28th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW), Bergen, Norway, 18-22 Oct 2025,
(doi: 10.1145/3710908)
Stumpf, Simone ORCID: https://orcid.org/0000-0001-6482-1973, Taka, Evdoxia
ORCID: https://orcid.org/0000-0001-7011-3367, Nakao, Yuri, Luo, Lin
ORCID: https://orcid.org/0000-0002-0310-3158, Sonoda, Ryosuke and Yokota, Takuya
(2024)
The Need for User-centred Assessment of AI Fairness and Correctness.
In: UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization, Cagliari, Italy, 01-04 Jul 2024,
pp. 523-527.
ISBN 9798400704666
(doi: 10.1145/3631700.3664912)