Public Perceptions of Fairness Metrics Across Borders
Not on the map. Why?
Abstract (arXiv)
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 relationship between personal attributes and the choice of fairness metrics, uncovering a significant influence of national context on these preferences.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 24 Mar 2024.
Builds on
13 of 41 references.
References · 13
- 2016Equality of Opportunity in Supervised Learningcited by 2,000
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
- 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
- 2017On Fairness and Calibrationcited by 406
- 2018Fairness of Exposure in Rankingscited by 189
- 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
- 2016Measuring Fairness in Ranked Outputscited by 81
- 2017Ranking with Fairness Constraintscited by 78
- 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
- 2018Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Predictioncited by 35
- 2021Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systemscited by 27
Built on by
Not cited yet.