research-article

Open access

Evaluating the Impact of Human Explanation Strategies on Human-AI Visual Decision-Making

Authors:

Katelyn Morrison,

Kenneth Holstein,

Adam PererAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 7, Issue CSCW1

Article No.: 48, Pages 1 - 37

https://doi.org/10.1145/3579481

Published: 16 April 2023 Publication History

Abstract

Artificial intelligence (AI) is increasingly being deployed in high-stakes domains, such as disaster relief and radiology, to aid practitioners during the decision-making process. Explainable AI techniques have been developed and deployed to provide users insights into why the AI made certain predictions. However, recent research suggests that these techniques may confuse or mislead users. We conducted a series of two studies to uncover strategies that humans use to explain decisions and then understand how those explanation strategies impact visual decision-making. In our first study, we elicit explanations from humans when assessing and localizing damaged buildings after natural disasters from satellite imagery and identify four core explanation strategies that humans employed. We then follow up by studying the impact of these explanation strategies by framing the explanations from Study 1 as if they were generated by AI and showing them to a different set of decision-makers performing the same task. We provide initial insights on how causal explanation strategies improve humans' accuracy and calibrate humans' reliance on AI when the AI is incorrect. However, we also find that causal explanation strategies may lead to incorrect rationalizations when AI presents a correct assessment with incorrect localization. We explore the implications of our findings for the design of human-centered explainable AI and address directions for future work.

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Index Terms

Evaluating the Impact of Human Explanation Strategies on Human-AI Visual Decision-Making
1. Applied computing
  1. Computers in other domains
2. Human-centered computing
  1. Collaborative and social computing

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 7, Issue CSCW1

CSCW

April 2023

3836 pages

EISSN:2573-0142

DOI:10.1145/3593053

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Jeff Nichols
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Published: 16 April 2023

Published in PACMHCI Volume 7, Issue CSCW1

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Morrison KSpitzer PTurri VFeng MKühl NPerer A(2024)The Impact of Imperfect XAI on Human-AI Decision-MakingProceedings of the ACM on Human-Computer Interaction10.1145/36410228:CSCW1(1-39)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3641022
Yen RSultanum NZhao J(2024)To Search or To Gen? Exploring the Synergy between Generative AI and Web Search in ProgrammingExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650867(1-8)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650867
Hao YLiu ZRiter RKalantari S(2024)Advancing Patient-Centered Shared Decision-Making with AI Systems for Older Adult Cancer PatientsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642353(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642353
Shakib MSolís PVarfalameyeva K(2024)Mapswipe for SDGs 3 & 13: take urgent cartographic action to combat heat vulnerability of manufactured and mobile home communitiesInternational Journal of Cartography10.1080/23729333.2024.2359074(1-23)Online publication date: 2-Jul-2024
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Gu HYang CMagaki SZarrin-Khameh NLakis NCobos IKhanlou NZhang XAssi JByers JHamza AHan KMeyer AMirbaha HMohila CStevens TStone SYan WHaeri MChen X(2024)Majority voting of doctors improves appropriateness of AI reliance in pathologyInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103315190(103315)Online publication date: Oct-2024
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Humer CHinterreiter ALeichtmann BMara MStreit M(undefined)Reassuring, Misleading, Debunking: Comparing Effects of XAI Methods on Human DecisionsACM Transactions on Interactive Intelligent Systems10.1145/3665647
https://dl.acm.org/doi/10.1145/3665647

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