Automation bias and assistive AI.

Khera, R., Simon, M. A., & Ross, J. S. (2023).

JAMA, 330(23), 2255. 

https://doi.org/10.1001/jama.2023.22557

At the point of care, artificial intelligence (AI) algorithms have been developed to augment diagnostic decisions and suggest appropriate care pathways, by leveraging complex information in a patient’s electronic health record, such as imaging, documentation, and diagnostic testing. With an increasing number of technologies integrated into the diagnosis, management, and even treatment of patients, the promise of AI to enhance accuracy, reduce errors, reduce clinician burnout, and improve clinical workflows may appear imminent.

MostAI algorithms aredesigned tobe assistive technologies—augmenting, not replacing, clinicians’

decision-making. AI models are imperfect and lack the broader clinical context that may be relevant for patient care. The expectation is that the diagnostic performance of clinicians supported by AI will exceed those of clinicians without such support.

A pdf is here.

Here are some thoughts:

This article highlights a critical problem with artificial intelligence in medicine: automation bias. This is when clinicians trust an AI’s recommendation too much, even when it is clearly wrong or contradicts their own judgment. The authors show that biased AI models can significantly lower the quality of patient care, and simply explaining how the AI works does not fix the issue. Clinicians, often working under time pressure, may defer to the tool instead of using their own expertise, which can lead to direct patient harm.

The key takeaway is that keeping a human “in the loop” is not enough to ensure safety. Current regulatory approaches focus too much on the AI’s technical accuracy and not enough on how real clinicians actually use these tools in practice. The authors argue that better training, higher safety standards, and truly interpretable AI are needed. Without these changes, the excitement around medical AI risks overshadowing its primary goal: improving patient care, not undermining it.

Exploring the frontiers of LLMs in psychological applications: a comprehensive review.

Ke, L., Tong, S., Cheng, P., & Peng, K. (2025).

Artificial Intelligence Review, 58(10).

https://doi.org/10.1007/s10462-025-11297-5

Abstract

This review explores the frontiers of large language models (LLMs) in psychological applications. Psychology has undergone several theoretical changes, and the current use of artificial intelligence (AI) and machine learning, particularly LLMs, promises to open up new research directions. We aim to provide a detailed exploration of how LLMs are transforming psychological research. We discuss the impact of LLMs across various branches of psychology—including cognitive and behavioral, clinical and counseling, educational and developmental, and social and cultural psychology—highlighting their ability to model patterns, cognition, and behavior similar to those observed in humans. Furthermore, we explore the ability of such models to generate coherent, contextually relevant text, offering innovative tools for literature reviews, hypothesis generation, experimental designs, experimental subjects, and data analysis in psychology. We emphasize the importance of addressing technical and ethical challenges, including data privacy, the ethics of using LLMs in psychological research, and the need for a deeper understanding of these models’ limitations. Researchers should use LLMs responsibly in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, this review provides a comprehensive overview of the current state of LLMs in psychology, exploring the potential benefits and challenges. We hope it can serve as a call to action for researchers to responsibly leverage LLMs’ advantages while addressing the associated risks.

Here is a great quote from the article: “LLM output should not be mistaken for the presence of thought but instead viewed as complex pattern matching based on probabilistic modeling.”

Here are some thoughts:

This review provides a timely and comprehensive framework for understanding how LLMs are transforming psychological research, organized around Newell's hierarchical timescales of human behavior. The authors strike an excellent balance between enthusiasm for LLMs' emergent abilities, such as analogical reasoning and emotion recognition, and a critical awareness of their fundamental limitations, including the lack of genuine understanding, persistent biases toward WEIRD populations, and risks in clinical applications like suicide risk assessment. The paper is particularly strong in its systematic presentation of empirical findings across cognitive, clinical, educational, and social psychology, supported by clear tables that make specific applications and results easily accessible to researchers. 

While the review covers LLMs as both research tools and simulated subjects, it could further explore the epistemological risks of circular validation where LLMs are used to study behaviors they merely replicate from training data. Additionally, greater attention to open source models and the inherent constraints of transformer architectures for real time or developmental processes would strengthen future work. Overall, this article serves as an essential resource for psychologists seeking to responsibly integrate LLMs into their research, offering both practical guidance and ethical guardrails without succumbing to technological hype.

Responsible Use of AI in Assessment

American Psychological Association

The information is here.

Summary

Artificial intelligence (AI) is increasingly used in psychological and educational assessment for tasks like scoring, summarizing, reporting, and pattern recognition. Thoughtful use of AI can improve efficiency, consistency, and service access. However, AI systems may introduce bias, errors, and lack transparency, so their risks must be carefully considered due to the significant impact of assessment decisions. While traditional considerations and evaluation criteria for practicing and researching assessment remain relevant, the integration of AI introduces unique factors that must be understood and addressed to ensure validity, reliability, fairness, and transparency.

To address these concerns, the members of APA’s Committee on Psychological Tests and Assessment (CPTA) have developed a concisely presented, comprehensive document that delves into the ethical and practical considerations for the use of AI in assessment across domains (e.g., clinical, I/O, school) and situations (e.g., employment testing, clinical evaluations). The document identifies considerations pertinent at specific decision-making junctures (e.g., tool selection, administration/delivery, scoring, interpretation, reporting) as well as considerations that apply across all assessment activities. The intended audience for this document is psychologists, including but not limited to health service psychologists and psychologists working in industry, academia, and public service positions as well as students of psychology. Although not the intended audience, this document may also serve as a resource for consumers of psychology and the public.

Principles for responsible AI use in assessment

Eight key areas to consider whenever AI is used in psychological assessment:

• Transparency and accountability
• Bias and fairness
• Privacy and confidentiality
• Informed consent
• Competence and training
• Human oversight
• Impact on applied and clinical work
• Continuous improvement

Principled by Design: Ethical Decision-making with Integrity

Gavazzi, J. (2026).

www.ethicalpsychology.com

This article is self-published for inclusion in a home study offered through the Pennsylvania Psychological Association. The home study promotes a structured approach to ethical decision-making, designed to support self-reflective practice.

Clinical Impact Statement

This article offers psychologists a practical, principle-based framework for working through ethical dilemmas in clinical practice. By treating autonomy, beneficence, nonmaleficence, justice, and fidelity as competing obligations to be specified and balanced rather than rules to be memorized, the framework helps clinicians reason transparently through situations in which the Ethics Code alone does not provide clear direction. It supports more defensible decisions, stronger therapeutic relationships, and the kind of reflective practice that treats ethics as an aspiration rather than a minimum standard.

The article is here.

The companion worksheet is here.