Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism
Open accessAbstract
Understanding how racial information impacts human decision making in online systems is critical in today's world. Prior work revealed that race information of criminal defendants, when presented as a text field, had no significant impact on users' judgements of recidivism. We replicated and extended this work to explore how and when race information influences users' judgements, with respect to the saliency of presentation. Our results showed that adding photos to the race labels had a significant impact on recidivism predictions for users who identified as female, but not for those who identified as male. The race of the defendant also impacted these results, with black defendants being less likely to be predicted to recidivate compared to white defendants. These results have strong implications for how system-designers choose to display race information, and cautions researchers to be aware of gender and race effects when using Amazon Mechanical Turk workers.
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Tasks that involve inherent uncertainty are often those where humans tend to rely on AI systems for advice, such as loan approval, recidivism prediction, house price estimation, and student admission.
“Tasks that involve inherent uncertainty are often those where humans tend to rely on AI systems for advice, such as loan approval [27, 34, 124], recidivism prediction [31, 47, 86], house price estimation [2, 13, 25], and student admission [13, 24].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [86]
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- Authors
- Mallari, K., Inkpen, K., Johns, P., Tan, S., Ramesh, D., & Kamar, E.
- Affiliations
- University of Washington (US)
- Microsoft (United States) (US)
- Cornell University (US)
- University of Michigan (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-13
- OpenAlex
- W3005080571
- Cited by
- 24
- References
- 53
- Citations by year
- 2026: 2 · 2024: 5 · 2023: 4 · 2022: 10 · 2021: 2 · 2020: 1
- FWCI
- 4.42
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Keywords
- recidivism prediction, racial bias, criminal defendants, salience, gender differences, Black defendants, online decision making, algorithmic fairness, Amazon Mechanical Turk
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