Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Human Cognitive Adaptation to High-Risk AI Systems under the EU AI Act: AI as an External Cognitive Regulator and Cognitive Safety Behavior

Authors: Valente, Stefano;

Human Cognitive Adaptation to High-Risk AI Systems under the EU AI Act: AI as an External Cognitive Regulator and Cognitive Safety Behavior

Abstract

The European Union Artificial Intelligence Act (EU AI Act) introduces a comprehensive regulatory framework for high-risk AI systems, particularly in healthcare and clinical decision support. While the regulation emphasizes technical robustness, transparency, and human oversight, it pays limited attention to how sustained interaction with AI systems may reshape human cognition over time. This paper argues that high-risk AI systems can function as external cognitive regulators, progressively influencing users’ decision-making processes, emotional regulation strategies, and sense of agency. Drawing on the extended mind framework and transdiagnostic cognitive-behavioral models, AI reliance is conceptualized as a potential cognitive safety behavior: a strategy that provides short-term relief from uncertainty and decisional burden while posing long-term risks of cognitive narrowing, avoidance, and reduced autonomy. Using high-risk clinical AI systems as a paradigmatic case, with particular reference to addiction care, the paper examines how these cognitive dynamics intersect with the assumptions embedded in the EU AI Act. The analysis highlights regulatory blind spots related to human cognitive adaptation and proposes directions for integrating cognitive and psychological considerations into AI governance and risk assessment.

Keywords

EU AI Act • High-risk AI systems • Human–AI interaction • Cognitive adaptation • Extended mind • Safety behaviors • Clinical AI • Addiction care • Decision-making • AI governance • Digital health • Human oversight, EU AI Act • High-risk AI systems • Human–AI interaction • Cognitive adaptation • Extended mind • Safety behaviors • Clinical AI • Addiction care • Decision-making • AI governance • Digital health • Human oversight

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average