How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness
arXiv:1811.03654
Abstract
What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people's perceptions of three of these fairness definitions. Across two online experiments, we test which definitions people perceive to be the fairest in the context of loan decisions, and whether fairness perceptions change with the addition of sensitive information (i.e., race of the loan applicants). Overall, one definition (calibrated fairness) tends to be more preferred than the others, and the results also provide support for the principle of affirmative action.
To appear at AI Ethics and Society (AIES) 2019
References in corpus (6)
- Equality of Opportunity in Supervised Learning
- The social dilemma of autonomous vehicles
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- On Formalizing Fairness in Prediction with Machine Learning
- Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
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Cited by in corpus (5)
- How Different Groups Prioritize Ethical Values for Responsible AI
- Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making
- Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
- Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
- Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making