A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
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Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives, some studies report improved user decision-making performance through XAI, while others report only negligible effects. Therefore, in this article, we present an initial synthesis of existing research on XAI studies using a statistical meta-analysis to derive implications across existing research. We observe a statistically positive impact of XAI on users' performance. Additionally, the first results indicate that human-AI decision-making tends to yield better task performance on text data. However, we find no effect of explanations on users' performance compared to sole AI predictions. Our initial synthesis gives rise to future research investigating the underlying causes and contributes to further developing algorithms that effectively benefit human decision-makers by providing meaningful explanations.
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The actual impact of explanations can be influenced by various factors, including task difficulty, decision-maker expertise, decision design, and the cognitive load required to comprehend the explanation, and also the type of explanations and the way it is presented.
“The actual impact of explanations can be influenced by various factors, including task difficulty [44], decision-maker expertise, decision design [45], and the cognitive load required to comprehend the explanation [46] and also the type of explanations and the way it is presented.”
From AI Trust: Can Explainable AI Enhance Warranted Trust? (2023) · cites this paper as [46]
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Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection?
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“According to recent reviews, multidisciplinary perspectives on this topic lead to confusion about notations and conceptual definitions of explanations in the context of AI (Schemmer et al. 2022; Vilone and Longo 2021).”
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- Authors
- Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., & Vössing, M.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2022-07-26
- Pages
- 617-626
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W4280639483
- Cited by
- 91
- References
- 45
- Citations by year
- 2026: 21 · 2025: 28 · 2024: 26 · 2023: 12 · 2022: 4
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by-nc-nd)
- KITopen
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- explainable AI, human-AI decision-making, AI-assisted decision-making, explanation, text data
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.
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