"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918357827059712 |
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| author | Luo, Lin Nakao, Yuri Chollet, Mathieu Inakoshi, Hiroya Stumpf, Simone |
| author_facet | Luo, Lin Nakao, Yuri Chollet, Mathieu Inakoshi, Hiroya Stumpf, Simone |
| contents | Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fairness. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 26 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness when placed in the role of deciding on features with priority, metrics, and thresholds. We reveal that stakeholders' fairness decisions are more complex than typical AI expert practices: they considered features far beyond legally protected features, tailored metrics for specific contexts, set diverse yet stricter fairness thresholds, and even preferred designing customized fairness. Our results extend the understanding of how stakeholders can meaningfully contribute to AI fairness governance and mitigation, underscoring the importance of incorporating stakeholders' nuanced fairness judgments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_17956 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | "I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment Luo, Lin Nakao, Yuri Chollet, Mathieu Inakoshi, Hiroya Stumpf, Simone Artificial Intelligence Human-Computer Interaction Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fairness. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 26 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness when placed in the role of deciding on features with priority, metrics, and thresholds. We reveal that stakeholders' fairness decisions are more complex than typical AI expert practices: they considered features far beyond legally protected features, tailored metrics for specific contexts, set diverse yet stricter fairness thresholds, and even preferred designing customized fairness. Our results extend the understanding of how stakeholders can meaningfully contribute to AI fairness governance and mitigation, underscoring the importance of incorporating stakeholders' nuanced fairness judgments. |
| title | "I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.17956 |