AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind
Open accessAbstract
Machine learning-based AI algorithms lack transparency. In this article, I offer an interpretation of AI’s explainability problem and highlight its ethical saliency. I try to make the case for the legal enforcement of a strong explainability requirement: human organizations which decide to automate decision-making should be legally obliged to demonstrate the capacity to explain and justify the algorithmic decisions that have an impact on the wellbeing, rights, and opportunities of those affected by the decisions. This legal duty can be derived from the demands of Rawlsian public reason. In the second part of the paper, I try to show that the argument from the limitations of human cognition fails to get AI off the hook of public reason. Against a growing trend in AI ethics, my main argument is that the analogy between human minds and artificial neural networks fails because it suffers from an atomistic bias which makes it blind to the social and institutional dimension of human reasoning processes. I suggest that developing interpretive AI algorithms is not the only possible answer to the explainability problem; social and institutional answers are also available and in many cases more trustworthy than techno-scientific ones.
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To meet all the social requirements of explanation will require maintaining the procedures and protocols that were already in place before the introduction of machine learning tools.
“Maclure (2021, p. 13) argues that to meet all the social requirements of explanation will”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Maclure, 2021
Section 5.1 says that maintaining pre-existing procedures and protocols will satisfy demands of public reason in many contexts. The claim extends this to all social requirements of explanation, which the passage does not support. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIf explainability in a full sense requires maintaining pre-existing procedures and protocols, enforcing it would undermine one of the main attractions of deploying AI systems, namely their capacity to function independently.
“But, as Maclure (2021, p. 14) sees, if this is what explainability, in a full sense, requires, enforcing it would undermine one of the main attractions of deploying AI systems, namely their capacity to function independently.”
From Hidden Risks: Artificial Intelligence and Hermeneutic Harm (2025) · cites this paper as Maclure, 2021
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- Authors
- Maclure, J.
- Affiliations
- McGill University (CA)
- Corresponding author
- Maclure, Jocelyn
- ORCID
- Maclure, Jocelyn
- Venue
- Minds and Machines
- Published
- 2021-08-23
- Volume
- 31
- Issue
- 3
- Pages
- 421-438
- ISSN
- 0924-6495 (ISSN-L)
- 1572-8641
- OpenAlex
- W3195523531
- Cited by
- 67
- References
- 69
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 15 · 2023: 16 · 2022: 5
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-11-09)
- Open access
- hybrid
- doi.org
- Locations
- Minds and Machines (publishedVersion, cc-by)
- Funders
- SSHRC (430-2019-01037)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- explainable AI, public reason, Rawlsian justice, algorithmic transparency, automated decision-making, AI ethics, algorithmic accountability
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