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

Wang, Ruotong; Harper, F. Maxwell; Zhu, Haiyi

doi:10.1145/3313831.3376813

Cited by 211Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)AI and HR Technologies

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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Previous work where a human supervised each decision made by the algorithmic system found no evidence that this configuration affected fairness perceptions.
“As opposed to some previous work where a human would supervise each decision made by the algorithmic system [103] -the authors did not fnd any evidence of this confguration afecting fairness perceptions-”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [103]
Earlier work found that case-by-case human intervention did not contribute to perceptions of fairness.
“These results resonate with earlier work on the topic [103], where a case-by-case human intervention did not contribute to perceptions of fairness.”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [103]
The design choice to tell the story in the third person with no reference to personal characteristics was made to minimize outcome favourability bias.
“The reason behind this design choice was to minimize, as far as possible, the outcome favourability bias [103].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [103]
This increased efficiency is likely to increase consumers' evaluations of chatbot-based results as more fair and objective.
“This increased efficiency is likely to increase consumers' evaluations of chatbot-based results as more fair and objective [70,71].”
From Consumer Responses to Generative AI Chatbots Versus Search Engines for Product Evaluation (2025) · cites this paper as [71]
The ability of AI chatbots to provide quick, complete, and direct answers to consumer queries is known to increase the perceived objectivity of the search results.
“Furthermore, the ability of AI chatbots to provide quick, complete, and direct answers to consumer queries is known to increase the perceived objectivity of the search results [71].”
From Consumer Responses to Generative AI Chatbots Versus Search Engines for Product Evaluation (2025) · cites this paper as [71]

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Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual DifferencesPreprint
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"I agree with the decision, but they didn't deserve this"; Future Developers' Perception of Fairness in Algorithmic Decisions
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HOW OUTCOMES AFFECT PERCEIVED FAIRNESS IN ALGORITHMIC VS. HUMAN DECISION-MAKING: AN EMPIRICAL STUDYPreprint
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I agree with the decision, but they didn't deserve this
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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: Social Sciences › Safety Research › 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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