A Study on Fairness and Trust Perceptions in Automated Decision Making
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Abstract (arXiv)
Automated decision systems are increasingly used for consequential decision making -- for a variety of reasons. These systems often rely on sophisticated yet opaque models, which do not (or hardly) allow for understanding how or why a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield undesirable (e.g., unfair) outcomes because their sanity is difficult to assess and calibrate in the first place. In this work, we conduct a study to evaluate different attempts of explaining such systems with respect to their effect on people's perceptions of fairness and trustworthiness towards the underlying mechanisms. A pilot study revealed surprising qualitative insights as well as preliminary significant effects, which will have to be verified, extended and thoroughly discussed in the larger main study.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 3 of its 38 references that are in the map. First posted on arXiv 8 Mar 2021.
Builds on
3 of 38 references.
References · 3
- 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
- 2019Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learningcited by 57
- 2020Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differencescited by 41
Built on by
Citing papers per year: 2021: 4; 2022: 2; 2023: 1.
7 papers.
- 2021Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literaturecited by 27
- 2022Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Makingcited by 25
- 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
- 2021Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systemscited by 9
- 2021Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Makingcited by 3
- 2021Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligencecited by 2
- 2022A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Makingcited by 1
- 2023A Critical Survey on Fairness Benefits of Explainable AI15 Oct 2023
- 2022Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making23 Sep 2022
- 2022A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making29 Apr 2022
- 2021Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence28 Sep 2021
- 2021Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making13 Sep 2021
- 2021Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems14 Aug 2021
- 2021Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature22 Mar 2021