Public Perceptions of Fairness Metrics Across Borders
Yuya Sasaki, Sohei Tokuno, Haruka Maeda, Kazuki Nakajima, Osamu, Sakura, George Fletcher, Mykola Pechenizkiy, Panagiotis Karras, Irina, Shklovski

TL;DR
This study conducts an international survey across four countries to evaluate public perceptions of fairness metrics in decision-making, revealing significant influence of national context on fairness preferences.
Contribution
It provides the first large-scale cross-national analysis of fairness metric perceptions, highlighting cultural differences in fairness judgments.
Findings
National context significantly influences fairness metric preferences.
Survey responses vary across China, France, Japan, and the US.
Personal attributes also impact fairness perceptions.
Abstract
Which fairness metrics are appropriately applicable in your contexts? There may be instances of discordance regarding the perception of fairness, even when the outcomes comply with established fairness metrics. Several questionnaire-based surveys have been conducted to evaluate fairness metrics with human perceptions of fairness. However, these surveys were limited in scope, including only a few hundred participants within a single country. In this study, we conduct an international survey to evaluate public perceptions of various fairness metrics in decision-making scenarios. We collected responses from 1,000 participants in each of China, France, Japan, and the United States, amassing a total of 4,000 participants, to analyze the preferences of fairness metrics. Our survey consists of three distinct scenarios paired with four fairness metrics. This investigation explores the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQualitative Comparative Analysis Research · Global trade, sustainability, and social impact · Ethics and Social Impacts of AI
