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Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences

Wang, Ruotong, Harper, F. Maxwell, Zhu, Haiyi·2020·Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

DOI 10.1145/3313831.3376813

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Abstract

Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of "outcome favorability" bias.

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Self-citation
Fair Machine Guidance to Enhance Fair Decision Making in Biased People
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Authors: Wang, R., Harper, F. M., & Zhu, H.
Affiliations: Carnegie Mellon University (US)
Amazon (United States) (US)
ORCID: Wang, Ruotong
Harper, F. Maxwell
Zhu, Haiyi
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Publisher: ACM
Published: 2020-04-21
Pages: 1-14
DOI: 10.1145/3313831.3376813
OpenAlex: W3029022080
Semantic Scholar: 6d847d92ed6efbcd322549f732c014e9bfb1c45d
Cited by: 211
References: 58
Citations by year: 2026: 30 · 2025: 37 · 2024: 37 · 2023: 34 · 2022: 36 · 2021: 34 · 2020: 3
FWCI: 27.75
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: CHI Conference on Human Factors in Computing Systems (publishedVersion)
Funders: National Science Foundation (IIS-2001851, IIS-2000782, IIS-1939606)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Keywords: perceived fairness, algorithmic decision-making, algorithmic fairness, algorithmic bias, individual differences, user feedback, educational attainment, gender differences

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Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences

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