“There Is Not Enough Information”: On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making
Open accessAbstract
Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. In this work, we conduct a human subject study to assess people’s perceptions of informational fairness (i.e., whether people think they are given adequate information on and explanation of the process and its outcomes) and trustworthiness of an underlying ADS when provided with varying types of information about the system. More specifically, we instantiate an ADS in the area of automated loan approval and generate different explanations that are commonly used in the literature. We randomize the amount of information that study participants get to see by providing certain groups of people with the same explanations as others plus additional explanations. From our quantitative analyses, we observe that different amounts of information as well as people’s (self-assessed) AI literacy significantly influence the perceived informational fairness, which, in turn, positively relates to perceived trustworthiness of the ADS. A comprehensive analysis of qualitative feedback sheds light on people’s desiderata for explanations, among which are (i) consistency (both with people’s expectations and across different explanations), (ii) disclosure of monotonic relationships between features and outcome, and (iii) actionability of recommendations.
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AI literacy has been proven to significantly affect perceptions of informational fairness.
“AI literacy has been proven to significantly afect perceptions of informational fairness [83].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [83]
Explanations do not moderate the effect of contestability on procedural fairness.
“However, contrary to our expectations, and to suggestions from earlier work [83], we did not fnd evidence that explanations moderate the efect of contestability on procedural fairness, i.e., help participants question structural aspects of the decision-making process such as the factors requested by the bank and how these are used.”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [83]
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“[83] found that outcome explanations would increase end users’ perceptions of informational fairness, but it would make them question structural aspects of the procedure, just as it was claimed by Greenberg [40] for human decision-making.”
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- Authors
- Schoeffer, J., Kuehl, N., & Machowski, Y.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- ORCID
- Schoeffer, Jakob
- Venue
- 2022 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2022-06-20
- Pages
- 1616-1628
- OpenAlex
- W4280563381
- Cited by
- 67
- References
- 72
- Citations by year
- 2026: 15 · 2025: 23 · 2024: 20 · 2023: 7 · 2022: 2
- FWCI
- 7.19
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- KITopen
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- automated decision systems, explainable AI, informational fairness, trustworthiness, AI literacy, explanation quality, model opacity, human-AI interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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