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<p>The integration of artificial intelligence (AI) into psychological assessment has driven significant methodological innovations, offering enhanced scalability, personalization, and efficiency. However, these developments present complex ethical challenges related to bias, transparency, data privacy, and accountability. This article provides a critical and integrative analysis of the ethical implications of AI in psychological contexts, synthesizing interdisciplinary research and relevant regulatory frameworks. Emphasizing the core pillars of fairness, interpretability, and informed consent, it examines risks including algorithmic bias, the opacity of machine learning models, and socio-technical challenges inherent in deploying AI in high-stakes psychological domains. Drawing on selected empirical studies and normative frameworks, the article articulates targeted strategic responses for stakeholders aiming to mitigate these ethical risks. Rather than presenting a prescriptive guide, the article outlines evidence-informed recommendations to support responsible AI adoption. These include strategies for inclusive data practices, transparent model design, interdisciplinary collaboration, and post-deployment accountability. By addressing both conceptual and practical dimensions, the article contributes to the development of ethically grounded AI integration in psychological assessment.</p>

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Ethical challenges and strategic responses to AI integration in psychological assessment

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Vitalii Shymko,
  • Anzhela Babadzhanova

摘要

The integration of artificial intelligence (AI) into psychological assessment has driven significant methodological innovations, offering enhanced scalability, personalization, and efficiency. However, these developments present complex ethical challenges related to bias, transparency, data privacy, and accountability. This article provides a critical and integrative analysis of the ethical implications of AI in psychological contexts, synthesizing interdisciplinary research and relevant regulatory frameworks. Emphasizing the core pillars of fairness, interpretability, and informed consent, it examines risks including algorithmic bias, the opacity of machine learning models, and socio-technical challenges inherent in deploying AI in high-stakes psychological domains. Drawing on selected empirical studies and normative frameworks, the article articulates targeted strategic responses for stakeholders aiming to mitigate these ethical risks. Rather than presenting a prescriptive guide, the article outlines evidence-informed recommendations to support responsible AI adoption. These include strategies for inclusive data practices, transparent model design, interdisciplinary collaboration, and post-deployment accountability. By addressing both conceptual and practical dimensions, the article contributes to the development of ethically grounded AI integration in psychological assessment.