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Manipulating and Measuring Model Interpretability
Open accessAbstract
With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed, there have been relatively few experimental studies investigating whether these models achieve their intended effects, such as making people more closely follow a model’s predictions when it is beneficial for them to do so or enabling them to detect when a model has made a mistake. We present a sequence of pre-registered experiments (N = 3, 800) in which we showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box). Predictably, participants who saw a clear model with few features could better simulate the model’s predictions. However, we did not find that participants more closely followed its predictions. Furthermore, showing participants a clear model meant that they were less able to detect and correct for the model’s sizable mistakes, seemingly due to information overload. These counterintuitive findings emphasize the importance of testing over intuition when developing interpretable models.
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“Some researchers measure subjective trust using questionnaires, while others measure objective trust related behavior such as reliance.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Poursabzi-Sangdeh et al., 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While WOA has been used in the past by researchers in XAI as an alternative trust measurement (Logg et al., 2019; Mucha et al., 2021; Poursabzi-Sangdeh et al., 2021), it has never been explicitly referred to as reliance and thus clearly differentiated from trust to the best of our knowledge.”
From Exploring the effects of human-centered AI explanations on trust and reliance (2023) · cites this paper as Poursabzi-Sangdeh et al., 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, across many domains, empirical studies of explanations have produced mixed results [4,11,49,53,58,68,82,87], with some suggesting that explanations increase decisionmakers' tendency to rely on AI predictions even when the AI system is wrong [4,68,82,87]-a phenomenon often referred to as overreliance [10,78].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [68]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“On the contrary, several studies found that showing feature-based explanations increases people's tendency to over-rely on the model when it is wrong, compared to showing only the AI predictions [4,68,82,87].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [68]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Work in the human–computer interaction community has identified that increased transparency can hamper users’ ability to detect sizable model errors and correct for them, “seemingly due to information overload””
From The false hope of current approaches to explainable artificial intelligence in health care (2021) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“and evidence suggests models that use explainability techniques can hamper people's ability to detect when a model makes serious mistakes”
From The false hope of current approaches to explainable artificial intelligence in health care (2021) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“However, many empirical studies on human-AI decision-making did not observe complementary performance [6,16,37,52,58,61,68,82,84,87].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [68]
The cited source says what the claim attributes to it.
In the citing paper
“and that providing too much detail may reduce user performance due to information overload [44].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [44]
The cited source says what the claim attributes to it.
In the citing paper
“a study by Poursabzi-Sangdeh et al. [48] found that more 'clear' models can actually lead to information overload and hinder people's ability to detect AI errors.”
From Improving Human-AI Collaboration With Descriptions of AI Behavior (2023) · cites this paper as Poursabzi-Sangdeh et al. [48]
The cited source says what the claim attributes to it.
In the citing paper
“However, they choose to use the task of real estate valuation, and how it relates to a decision and an outcome of interest and the utility of explanations in achieving that goal was not established.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Poursabzi-Sangdeh et al. (2021)
The cited source says what the claim attributes to it.
In the citing paper
“Algorithmic transparency similarly reduces people's ability to detect and correct model errors”
From The flaws of policies requiring human oversight of government algorithms (2022) · cites this paper as Poursabzi-Sangdeh et al., 2021
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In the citing paper
“The complexity of tasks plays a signifcant role in determining the degree of reliance on AI advice, consistent with the fndings of [9, 105].”
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 [105]
Experimental — accuracy not yet measuredIn the citing paper
“Such explanations often do not match the end-users' mental models [56], making the explanations ineective [2,40,65] and at times even harmful [13,28,30,47,51].”
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 [40]
In the citing paper
“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 [40]
In the citing paper
“Many studies have found that cognitive biases (particularly anchoring bias and automation bias more broadly) can lead users to misunderstand model behavior and place unwarranted trust in AI systems [20, 34, 52, 55].”
From Interactive Model Cards: A Human-Centered Approach to Model Documentation (2022) · cites this paper as [55]
In the citing paper
“Previous works show that the availability of AI's explanations, regardless of their correctness, is likely to increase people's trust in AI [6,54,81,100].”
From Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making (2023) · cites this paper as [81]
In the citing paper
“Many studies have investigated the role of AI in assisting humans in making individual decisions [9,16,36, 37, 50, 51, 55, 58, 69, 83, 84, 89], e.g., predicting whether a person will recidivate in the near future.”
From Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation (2022) · cites this paper as [69]
In the citing paper
“However, recent research has shown that the recommendation-driven explainable/interpretable AI has little effect on decision making [1, 6-8, 28, 32], although this is not always the case [22,23,41].”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [32]
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Metadata
- Authors
- Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H.
- Affiliations
- Microsoft (United States) (US)
- Microsoft Research, United States
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-52
- OpenAlex
- W2788362053
- Semantic Scholar
- 2f4e42ab1368b83e38e3011fcec4edb57267b5d2
- Cited by
- 728
- References
- 116
- Citations by year
- 2026: 94 · 2025: 97 · 2024: 118 · 2023: 106 · 2022: 111 · 2021: 90
- FWCI
- 48.67
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- model interpretability, explainable AI, black-box models, model transparency, feature count, error detection, information overload, trust in machine learning, pre-registered experiment
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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