METHODS article
Front. Neurosci.
Sec. Neuroscience Methods and Techniques
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1697053
This article is part of the Research TopicAdvances in Explainable Analysis Methods for Cognitive and Computational NeuroscienceView all 4 articles
Auditing Cognitive Drift in AI-Driven Recommendation: A Responsible AI Methods Protocol with a Health Case Demonstration
Provisionally accepted
Zhuowei Li1*
Congqian Zhu2*- 1Universitat Zurich Wirtschaftswissenschaftliche Fakultat, Zürich, Switzerland
- 2Shenzhen University of Advanced Technology, Shenzhen, China
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We propose a protocol to detect and track cognitive drift caused by algorithmic curation. We confirm that the protocol is interpretable, sensitive, reproducible, and portable across domains. It is especially suited to cognitive and neurocognitive research. We build a Cognitive Drift Index (CDI), confirm its three dimensions, and use a small calibration run to set reasonable ranges. We then map CDI to governance action bands. Using health short videos as a case, we estimate path effects with a weighted least squares ANOVA and test robustness. The steps lead to the same pattern, which supports the protocol 's design and practical use. We show each component alongside the composite and sort totals into action bands so practitioners and responsible-AI teams can choose proportionate actions. We also consider using the method, as a conceptual tool, with eye-tracking or EEG to enable multimodal validation.
Keywords: Cognitive drift, algorithmic recommendation, Explainable measurement, Platform governance, Responsible artificial intelligence, Health Communication
Received: 01 Sep 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 Li and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhuowei Li, zhuowei.li@uzh.ch
Congqian Zhu, cqzhu2022@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.