No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML
Abstract
Automatically generated explanations of how machine learning (ML) models reason can help users understand and accept them. However, explanations can have unintended consequences: promoting over-reliance or undermining trust. This paper investigates how explanations shape users' perceptions of ML models with or without the ability to provide feedback to them: (1) does revealing model flaws increase users' desire to "fix" them; (2) does providing explanations cause users to believe - wrongly - that models are introspective, and will thus improve over time. Through two controlled experiments - varying model quality - we show how the combination of explanations and user feedback impacted perceptions, such as frustration and expectations of model improvement. Explanations without opportunity for feedback were frustrating with a lower quality model, while interactions between explanation and feedback for the higher quality model suggest that detailed feedback should not be requested without explanation. Users expected model correction, regardless of whether they provided feedback or received explanations.
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When individuals are faced with tasks that have lower uncertainty, such as annotation and classification tasks, they tend to rely less on AI advice and rely more on their expertise and judgment.
“On the other hand, when individuals are faced with tasks that have lower uncertainty, such as annotation and classifcation task [4, 77, 117], they tend to rely less on the AI advice and rely more on their expertise and judgment.”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [117]
Fewer studies examine how practitioners evaluate criteria such as basic functionality, safety, privacy, or recourse, just as fewer tools exist for this purpose.
“While there are multiple studies on the use of tools for fairness evaluation [54, 70, 119] and explainability [1, 46, 72, 82, 85, 98], fewer studies examine how practitioners evaluate other criteria such as basic functionality [117], safety, privacy, or recourse, just as fewer tools exist for this purpose.”
From Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling (2025) · cites this paper as [82]
Naive application of existing XAI methods may be ineffective for a broader audience of end-users, if not harmful.
“naive application of existing XAI methods may be ineective [2,40,65] to broader audience of end-users, if not harmful [28,47,49,51].”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [49]
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- Authors
- Smith-Renner, A., Fan, R., Birchfield, M., Wu, T., Boyd-Graber, J., Weld, D. S., & Findlater, L.
- Affiliations
- University of Maryland, College Park, MD, USA
- University of Washington (US)
- University of Maryland, College Park (US)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-13
- OpenAlex
- W3030350595
- Cited by
- 112
- References
- 60
- Citations by year
- 2026: 7 · 2025: 22 · 2024: 27 · 2023: 17 · 2022: 18 · 2021: 18 · 2020: 3
- FWCI
- 5.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- NSF (IIS-1822494, IIS-1409287)
- ONR (B99914-18-1-2193)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- explainable AI, user feedback, model accountability, trust in machine learning, user frustration, model introspection, human-machine interaction
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