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Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences
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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“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Metadata
- Authors
- Wang, R., Harper, F. M., & Zhu, H.
- Affiliations
- Carnegie Mellon University (US)
- Amazon (United States) (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-14
- 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
- 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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