2023 ACM Conference on Fairness Accountability and Transparency
Abstract
Explanations are hypothesized to improve human understanding of machine learning models and achieve a variety of desirable outcomes, ranging from model debugging to enhancing human decision making. However, empirical studies have found mixed and even ...
Integrity signals
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Cites
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Radensky et al. found that accurate confidence signals led to the greatest increase in trust without encouraging overreliance, though underreliance may still occur.
“Experimenting with different levels of calibration, they found that accurate confidence signals led to the greatest increase in trust without encouraging overreliance, though underreliance may still occur.”
From "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust (2024) · cites this paper as Radensky et al. [87]
Radensky et al. found that trust intentions and trust beliefs can be differently impacted by a system's expressed confidence.
“In general, the two are positively related [42], but Radensky et al. [87] found that they can be differently impacted by a system's expressed confidence.”
From "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust (2024) · cites this paper as Radensky et al. [87]
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Cited by 61
- Publisher
- ACM
- Published
- 2023-06-12
- OpenAlex
- W4380322001
- Cited by
- 61
- References
- 0
- Citations by year
- 2026: 3 · 2025: 27 · 2024: 15 · 2023: 11 · 2022: 3 · 2021: 2
- Type
- paratext
- Language
- en
- Open access
- closed
- 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 › Software › Software Testing and Debugging Techniques
- Keywords
- explanation, explainable machine learning, model debugging, human decision-making, fairness, accountability, transparency
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.
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