Cited by 69Digitalization, Law, and RegulationEthics and Social Impacts of AIArtificial Intelligence in Law

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index; none of the 85 sources its claims cite is checked for support yet.

Automation Bias in the AI Act: On the Legal Implications of Attempting to De-Bias Human Oversight of AI

Open access
Laux, Johann, Ruschemeier, Hannah·2025·European Journal of Risk Regulation

Abstract

This paper examines the legal implications of the explicit mentioning of automation bias (AB) in the Artificial Intelligence Act (AIA). The AIA mandates human oversight for high-risk AI systems and requires providers to enable awareness of AB, i.e., the human tendency to over-rely on AI outputs. The paper analyses the embedding of this extra-juridical concept in the AIA, the asymmetric division of responsibility between AI providers and deployers for mitigating AB, and the challenges of legally enforcing this novel awareness requirement. The analysis shows that the AIA’s focus on providers does not adequately address design and context as causes of AB, and questions whether the AIA should directly regulate the risk of AB rather than just mandating awareness. As the AIA’s approach requires a balance between legal mandates and behavioural science, the paper proposes that harmonised standards should reference the state of research on AB and human-AI interaction, holding both providers and deployers accountable. Ultimately, further empirical research on human-AI interaction will be essential for effective safeguards.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper analyzes the AI Act’s explicit reference to automation bias (AB) in Article 14(4b), arguing that the provider-focused duty to enable awareness of AB does not match the deployer-controlled causal factors and is difficult to enforce legally. It proposes that harmonised standards reference the state of research on AB and human-AI interaction, with responsibilities for both providers and deployers.

Key findings

  • The AIA mentions AB only in Article 14(4b), obliging providers to enable human oversight agents to “remain aware” of the tendency to over-rely on AI outputs; Article 26(2) imposes deployer duties on competence, training, and support but does not explicitly mention AB, though the paper argues deployers are plausibly obliged insofar as they control AB’s causal factors.
  • AB is multifactorial: provider-controlled design/interface/instructions and deployer-controlled individual, motivational, organisational, and contextual factors both matter, so the AIA’s asymmetric provider focus does not adequately address design and context as causes of AB.
  • Enforcement is challenging because “awareness” is a subjective internal state, a single decision rarely proves AB, ex post experimental re-enactments are likely prohibitively costly and time-consuming, and expert testimony is presented as the most probable proof route; the paper also notes that the unbiased “true outcome” is often unavailable, using a hiring example where a provider’s 80% selection-rate threshold flags female selection at 0.30 versus male at 0.40 (75% of the best-performing group) with ten male and ten female candidates and three female/four male invited.
  • The paper identifies a protection gap in Article 6(3) AIA: its high-risk exception relies on provider self-assessment and does not reflect the AB concern, and it suggests requiring an explicit reference to Article 14(4b) safeguards in that self-assessment; it also notes the Commission assessed that 5–15% of AI systems on the EU market are high-risk.

Methods

  • Legal-doctrinal analysis of the AIA, including Article 14(4b), Article 26(2), Article 6(3), legislative history, and the relationship with Article 22 GDPR and the CJEU’s SCHUFA decision.
  • Draws on psychological and human-AI interaction literature on AB, cognitive biases, and algorithm aversion, plus illustrative examples such as the hiring/fairness scenario from Langer et al.
  • Main analysis is normative and interpretive: it assesses the division of responsibility between providers and deployers, the justiciability of the awareness requirement, and proposes harmonised standards with a state-of-research clause.

Limitations

  • Stated: the paper repeatedly notes scientific uncertainty about the causes, remedies, and interactions of AB in high-risk AI, that psychological research on human-AI interaction is complex and dynamic, and that legal scholars disagree on how law can reduce cognitive biases.
  • Stated: enforcement limitations include difficulty proving AB occurred, the insufficiency of a single decision, costly ex post re-enactments, debate over the predictive value of the IAT, and unstable ground truth for fairness/non-discrimination and legal proceedings.
  • Evident in the text: the analysis is conceptual/doctrinal rather than empirical, relies on secondary literature and examples, and notes that harmonised standards addressing AB have not yet been published.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1017/err.2025.10033

Cites

0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

72 Sources not found · 66 Own interpretation · 10 No basis given · 1 Own result

  • Probably not a substantial claim. 68
  • It may state two separate claims as one. 27
85 of 85
  • The authors state this as an assumption.

    High-risk AI systems will have great practical relevance for the European single market.

    “It is reasonable to assume that high-risk systems will have great practical relevance for the European single market.”
  • The authors state this as an assumption.

    The factors that cause automation bias generally also apply to automation bias in human oversight of high-risk AI systems.

    “Assuming that these factors also apply to AB for human oversight, it must be said that the provider cannot control for them all.”
  • The authors state this as an assumption.

    Overcorrection can pose risks to safety, health, and fundamental rights.

    “(assuming that overcorrection can pose risks to safety, health, and fundamental rights)”
  • The paper relies on this without support.

    A legal procedure will typically focus on a specific case of oversight failure that may involve a single human decision or multiple human decisions.

    “A legal procedure will typically focus on a specific case of oversight failure that may involve a single human decision or multiple human decisions.”
  • The paper relies on this without support.

    Establishing the occurrence of psychological bias usually requires multiple data points.

    “Establishing the occurrence of psychological bias usually requires multiple data points.”
  • The paper relies on this without support.

    Raising awareness of AB reduces the likelihood of AB.

    “if providers and deployers are obliged to effectively raise awareness of AB and thus reduce the likelihood of AB”
6 of 6

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

3 of 3

Citations

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 5

  1. JJ Rachlinski and SL Johnson, "Does Unconscious Racial Bias Affect Trial Judges" (2009) 84 Notre Dame Law Review 1195.
  2. J Agerström and D-O Rooth, "The Role of Automatic Obesity Stereotypes in Real Hiring Discrimination" (2011) 96 Journal of Applied Psychology 790; FL Oswald and Others, "Predicting Ethnic and Racial Discrimination: A Meta- Analysis of IAT Criterion Studies" (2013) 105 Journal of Personality and Social Psychology 171.
  3. T Tridimas, The General Principles of EU Law (2. ed., 1. publ. in paperback, Oxford, Oxford University Press 2007) p 216. 38 Tridimas (supra note 35) 216. 39 Laux (n 20) 240-64. The EU AI Office could play an important role in monitoring the field of psychological research on human-AI interaction, see H Pouget and J Laux, "A Letter to the EU's Future AI Office" (3 October 2023) available at <https://carnegieendowment.org/2023/10/03/letter-to-eu-s-future-ai-office-pub-90683> (last accessed 25 January 2024). 40 AIA Annex IV 3.
  4. M Langer, K Baum and N Schlicker, "Effective Human Oversight of AI-Based Systems: A Signal Detection Perspective on the Detection of Inaccurate and Unfair Outputs" (2024) 35 Minds and Machines 1. 42 This 80% rule is taken from the example in: L Alexander, Making Decisions about Adverse Impact: The Influence of Individual and Situational Differences (Rice University 2022) available at <https://hdl.handle.net/1911/114147> accessed 15 January 2025. Cited after: Langer, Baum and Schlicker (supra note 39). 43 Langer, Baum and Schlicker (supra note 39) 12-13. 44 Ibid., 13. 45
  5. Wachter, Mittelstadt and Russell (supra note 14). 46 Ibid. 47 On this problem and jury decisions, see: J Elster, Securities against Misrule: Juries, Assemblies, Elections (New York, Cambridge University Press 2013). For an application to the EU legal system, see: Laux (n 20). For the context of human oversight, see: Langer, Baum and Schlicker (supra note 39) 12. 48 JA Jolowicz, On Civil Procedure (Cambridge, Cambridge University Press 2000) p 86.

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Metadata

Authors
Laux, J., & Ruschemeier, H.
Affiliations
  • University of Oxford (GB)
  • FernUniversität in Hagen (DE)
Corresponding author
Laux, Johann
Venue
European Journal of Risk Regulation
Published
2025-07-28
Volume
16
Issue
4
Pages
1519-1534
ISSN
  • 1867-299X (ISSN-L)
  • 2190-8249
OpenAlex
W4412687403
Cited by
69
References
0
Citations by year
2026: 58 · 2025: 11
FWCI
149.60
Type
journal article
Language
en
Open access
Locations
  • European Journal of Risk Regulation (publishedVersion, cc-by)
  • SSRN Electronic Journal (submittedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Law › Artificial Intelligence in Law
Keywords
automation bias, EU AI Act, human oversight of AI, high-risk AI systems, human-AI interaction, harmonised standards, AI regulation

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