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A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

Authors:

Patrick Hemmer,

Maximilian Nitsche,

Michael VössingAuthors Info & Claims

AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Pages 617 - 626

https://doi.org/10.1145/3514094.3534128

Published: 27 July 2022 Publication History

Abstract

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.

Supplementary Material

MP4 File (aies008.mp4)

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.

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Cited By

Nannini LHuyskes DPanai EPistilli GTartaro A(2025)Nullius in Explanans: an ethical risk assessment for explainable AIEthics and Information Technology10.1007/s10676-024-09800-727:1Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1007/s10676-024-09800-7
Mantri ISasikumar N(2025)Trustworthy Hybrid Decision-MakingMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_17(239-244)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74627-7_17
Marusich LBakdash JZhou YKantarcioglu MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Using AI uncertainty quantification to improve human decision-makingProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693491(34949-34960)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693491
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Index Terms

A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Conferences

AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

July 2022

939 pages

ISBN:9781450392471

DOI:10.1145/3514094

General Chairs:
Vincent Conitzer
Duke University & University of Oxford
,
John Tasioulas
University of Oxford
,
Program Chairs:
Matthias Scheutz
Tufts University
,
Ryan Calo
University of Washington
,
Martina Mara
Johannes Kepler University Linz
,
Annette Zimmermann
University of York

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Published: 27 July 2022

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AIES '22: AAAI/ACM Conference on AI, Ethics, and Society

May 19 - 21, 2021

Oxford, United Kingdom

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Cited By

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https://dl.acm.org/doi/10.1007/s10676-024-09800-7
Mantri ISasikumar N(2025)Trustworthy Hybrid Decision-MakingMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_17(239-244)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-74627-7_17
Marusich LBakdash JZhou YKantarcioglu MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Using AI uncertainty quantification to improve human decision-makingProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693491(34949-34960)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693491
Mingo H(2024)The Emerging Cybersecurity Challenges With Artificial IntelligenceMultisector Insights in Healthcare, Social Sciences, Society, and Technology10.4018/979-8-3693-3226-9.ch010(163-185)Online publication date: 5-Jan-2024
https://doi.org/10.4018/979-8-3693-3226-9.ch010
Lammert ORichter BSchütze CThommes KWrede B(2024)Humans in XAI: increased reliance in decision-making under uncertainty by using explanation strategiesFrontiers in Behavioral Economics10.3389/frbhe.2024.13770753Online publication date: 8-Mar-2024
https://doi.org/10.3389/frbhe.2024.1377075
Shakir MAbass HJelwy OAl-Bayati HSalman SMikhav VBodnar N(2024)Developing Interpretable Models for Complex Decision-Making2024 36th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT64283.2024.10749922(66-75)Online publication date: 30-Oct-2024
https://doi.org/10.23919/FRUCT64283.2024.10749922
Souchet AAmokrane-Ferka KBurkhardt J(2024)AI-assistance to decision-makers: evaluating usability, induced cognitive load, and trust's impactProceedings of the European Conference on Cognitive Ergonomics 202410.1145/3673805.3673845(1-4)Online publication date: 8-Oct-2024
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Zhang ZBuchner FLiu YButz A(2024)You Can Only Verify When You Know the Answer: Feature-Based Explanations Reduce Overreliance on AI for Easy Decisions, but Not for Hard OnesProceedings of Mensch und Computer 202410.1145/3670653.3670660(156-170)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1145/3670653.3670660
Humer CHinterreiter ALeichtmann BMara MStreit M(2024)Reassuring, Misleading, Debunking: Comparing Effects of XAI Methods on Human DecisionsACM Transactions on Interactive Intelligent Systems10.1145/366564714:3(1-36)Online publication date: 22-May-2024
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Li WBrooks CJoyner DKim MWang XXia M(2024)Influence on Judgements of Learning Given Perceived AI AnnotationsProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662044(221-231)Online publication date: 9-Jul-2024
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