Artificial intelligence and the value of transparency
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In decision-support settings, feedback signals can be derived from occurrences where human controllers reject system predictions.
“In decision-support settings, such signals can also be derived from occurrences where human controllers reject system predictions (Walmsley, 2021).”
From Contestable AI by Design: Towards a Framework (2022) · cites this paper as Walmsley, 2021
The composite and gradient nature of openness can be grounded directly in prior work on openness and accountability in AI systems.
“The composite and gradient nature of openness can be grounded directly in prior work on openness and accountability in AI systems [29, 37, 42, 58].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [58]
Work on AI safety and reproducibility has long pointed out the crucial importance of training data for understanding model performance, ensuring reproducibility, and assessing legal exposure.
“Work on AI safety and reproducibility has long pointed out the crucial importance of training data for understanding model performance, ensuring reproducibility, and assessing legal exposure [2, 25, 29, 46, 58].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [58]
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- Authors
- Walmsley, J.
- Affiliations
- University College Cork (IE)
- Corresponding author
- Walmsley, Joel
- ORCID
- Walmsley, Joel
- Venue
- AI & SOCIETY
- Published
- 2020-09-08
- Volume
- 36
- Issue
- 2
- Pages
- 585-595
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W3084135104
- Cited by
- 131
- References
- 31
- Citations by year
- 2026: 34 · 2025: 38 · 2024: 26 · 2023: 14 · 2022: 12 · 2021: 6 · 2020: 1
- FWCI
- 11.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- AI & Society (publishedVersion)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion)
- Cork Open Research Archive (University College Cork, Ireland) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- machine learning, big data, epistemology of AI, AI ethics, contestability, accountability, explainability, algorithmic decision-making
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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