ICYMI: APA Guidelines for Clinical Supervision in Health Service Psychology

APA Task Force on Clinical Supervision in

Health Service Psychology

Approved by APA Council or Representative August 2025

Preface

This document outlines revised Guidelines for Clinical Supervision of students in health service psychology education and training programs. The goal was to capture optimal performance expectations for psychologists who supervise and those preparing to supervise. It is based on the premise that supervisors a) strive to achieve competence in the provision of clinical supervision, b) employ a competency-based, metatheoretical approach to the clinical supervision process, and c) clinical supervision is a distinct professional competence that requires dedicated training.

The initial Guidelines for Clinical Supervision were developed as a resource to inform education and

training regarding the implementation of competency-based supervision and were approved by the American Psychological Association (APA) Council of Representatives in 2014. These revised Guidelines for Clinical Supervision build on the robust literature on competency-based education and clinical supervision. They are organized around six domains: supervisor competence; multicultural orientation; relationships; teaching and learning strategies; problems of professional competence, and ethical, legal, and regulatory considerations. These updated Guidelines for Clinical Supervision represent the collective effort of the original task force (2014), and a working group convened in 2024 by the APA Board of Educational Affairs. 

The Guidelines are here.

LLM-as-a-Supervisor: Mistaken therapeutic behaviors trigger targeted supervisory feedback.

Xu, C., Lv, Z., Lan, T. et al. (2025).

ArXiv.org.

https://doi.org/10.48550/arxiv.2508.09042

Abstract

Although large language models (LLMs) hold significant promise in psychotherapy, their direct application in patient-facing scenarios raises ethical and safety concerns. Therefore, this work shifts towards developing an LLM as a supervisor to train real therapists. In addition to the privacy of clinical therapist training data, a fundamental contradiction complicates the training of therapeutic behaviors: clear feedback standards are necessary to ensure a controlled training system, yet there is no absolute "gold standard" for appropriate therapeutic behaviors in practice. In contrast, many common therapeutic mistakes are universal and identifiable, making them effective triggers for targeted feedback that can serve as clearer evidence. Motivated by this, we create a novel therapist-training paradigm: (1) guidelines for mistaken behaviors and targeted correction strategies are first established as standards; (2) a human-in-the-loop dialogue-feedback dataset is then constructed, where a mistake-prone agent intentionally makes standard mistakes during interviews naturally, and a supervisor agent locates and identifies mistakes and provides targeted feedback; (3) after fine-tuning on this dataset, the final supervisor model is provided for real therapist training. The detailed experimental results of automated, human and downstream assessments demonstrate that models fine-tuned on our dataset MATE, can provide high-quality feedback according to the clinical guideline, showing significant potential for the therapist training scenario.

Here are some thoughts:

The paper convincingly shows that LLMs can learn to spot common therapeutic errors and generate useful corrective feedback. That's a real win for training. But the art of supervision (knowing when to confront, when to support, and how to nurture a therapist's unique voice) remains a human skill. For now.

Can AI technologies support clinical supervision? Assessing the potential of ChatGPT.

Cioffi, V., Ragozzino, O. et al. (2025).

Informatics, 12(1), 29. 

https://doi.org/10.3390/informatics12010029

Abstract

Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using ChatGPT-4 as a supervisory tool in psychotherapy training. To achieve this, a clinical case was presented to three distinct groups (untrained AI, pre-trained AI, and qualified human supervisor), and their

feedback was evaluated by Gestalt psychotherapy trainees using a Likert scale rating of satisfaction. Statistical analysis, using the statistical package SPSS version 25 and applying principal component analysis (PCA) and one-way analysis of variance (ANOVA), demonstrated significant differences in favor of pre-trained AI feedback. PCA highlighted four components of the questionnaire: relational and emotional (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction obtained from the three kinds of supervisory feedback were compared using ANOVA. The feedback generated by the pre-trained AI (f2)

was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3)) in C4; in C1, the superiority of f2 over f1 but not over f3 appears significant. These results suggest that AI, when appropriately calibrated, may be an appreciable tool for complementing the effectiveness of clinical supervision, offering an innovative blended supervision methodology, in particular in the area of career guidance.

Here are some thoughts:

This study is a proof-of-concept that AI, when carefully calibrated, can add value to clinical supervision, particularly in the relational and supportive dimensions. The most responsible path forward is strategic, ethical, and skeptical experimentation, by using AI as a low-stakes reflective mirror and a source of immediate emotional support, while firmly reserving the challenging and nuanced work of true professional growth for your human colleagues. The future is likely augmented supervision, not automated supervision.

A review of neuro-symbolic AI integrating reasoning and learning for advanced cognitive systems

Nawaz, U., Anees-Ur-Rahaman, M., & Saeed, Z. (2025).

Intelligent Systems With Applications, 26, 200541.

https://doi.org/10.1016/j.iswa.2025.200541

Abstract

Neuro-symbolic AI represents the convergence of two principal paradigms in artificial intelligence: neural networks, which are efficient in data-driven learning, and symbolic reasoning, which offers explainability and logical inference. This hybrid methodology combines the adaptability of neural networks with symbolic AI's interpretability and formal reasoning abilities, which provide a practical framework for advanced cognitive systems. This paper analyzes the present condition of neuro-symbolic AI, emphasizing essential techniques that combine reasoning and learning. We explore models such as Logic Tensor Networks, Differentiable Logic Programs, and Neural Theorem Provers. The study analyzes their impact on the advancement of cognitive systems in natural language processing, robotics, and decision-making. The paper examines the challenges faced by neuro-symbolic AI, such as scalability, integration with multimodal data, and maintaining interpretability without compromising efficiency. By evaluating the strengths and weaknesses of many methodologies, we comprehensively understand the field's development and its potential to revolutionize intelligent systems. In addition, we identify emerging research areas, including the incorporation of ethical frameworks and the development of adaptive dynamic neuro-symbolic systems that respond in real-time. This review aims to guide future research by providing insights into the potential of neuro-symbolic AI to influence the development of the next generation of intelligent, explainable, and adaptive systems.

Here are some thoughts:

This research is important because it provides a comprehensive, state-of-the-art analysis of the most promising path forward for creating truly intelligent, reliable, and understandable AI systems. It acknowledges the power of deep learning while rigorously addressing its most critical shortcomings—lack of reasoning, explainability, and data efficiency. For anyone working on or relying on AI in critical areas like medicine, finance, or autonomous systems, understanding neuro-symbolic AI is becoming essential.

Neuro-Symbolic AI is a hybrid approach to artificial intelligence that combines neural networks (which learn patterns from data) with symbolic reasoning (which uses logic and rules to think and explain decisions). In decision-science terms, this process is merging Type 1 and Type 2 thinking in order to reason more coherently.

In equation format: Neuro-Symbolic AI = Neural Learning (pattern recognition) + Symbolic Reasoning (logic & explainability).

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 are some thoughts:

LLMs as assistants, not replacements. They help with emotion recognition, risk flagging, and prognosis—but tend to underestimate risk in sensitive cases. Use them to prompt, not conclude, your clinical judgment.

The empathy gap is shrinking, but uneven. GPT-4 outperformed most humans on emotional intelligence measures, and AI feedback boosted peer empathy by nearly 20%. However, this is pattern recognition, not genuine attunement—critical when nonverbal cues matter.

Cognitive biases affect LLMs too. They show anchoring, representativeness, and cultural biases favoring WEIRD populations. This can subtly disadvantage clients from non-Western or underrepresented backgrounds.

Domain-specific training helps. The ChatCounselor model, trained on real therapy conversations, outperformed general-purpose models. Off-the-shelf tools are poor substitutes for clinically-trained ones.

Research gains are real. LLMs aid literature synthesis, hypothesis generation, and drafting documentation. But outputs must be verified—errors and misattributions are easy to miss.

Ethical infrastructure lags. Privacy, informed consent, and diagnostic bias remain unresolved. Treat AI as an adjunct to professional judgment and be transparent with clients.

Bottom line: LLMs are useful but evolving faster than ethical and clinical frameworks. Engage thoughtfully—neither dismiss nor uncritically adopt them—to stay ahead as the landscape shifts.

How malicious AI swarms can threaten democracy

Schroeder, D. T., et al. (2026).

Science, 391(6783), 354–357.

https://doi.org/10.1126/science.adz1697

Abstract

Advances in artificial intelligence (AI) offer the prospect of manipulating beliefs and behaviors on a population-wide level (1). Large language models (LLMs) and autonomous agents (2) let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility (3) and inexpensively create falsehoods that are rated as more human-like than those written by humans (3, 4). Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multiagent architectures (2), these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

A pdf is here.

Here are some thoughts:

The article argues that combining LLMs with multiagent architectures creates "malicious AI swarms" — a major leap beyond older botnets. These swarms can autonomously coordinate thousands of AI personas, precisely target vulnerable communities, mimic human behavior to evade detection, self-optimize in real time, and maintain persistent influence over long periods. The democratic harms are wide-ranging: fabricated consensus, deepened social fragmentation, contaminated AI training data, coordinated harassment, and eroded institutional trust that could make authoritarian measures seem acceptable. The authors call for a multilayered defense — continuous detection systems, user-facing "AI shields," stronger cryptographic identity standards, and a global AI Influence Observatory — while emphasizing that voluntary compliance will fall short as long as platforms' commercial incentives reward the same engagement dynamics that swarms exploit.

Exploring spiking neural networks for deep reinforcement learning in robotic tasks

Zanatta, L., et al. (2024).

Scientific Reports, 14(1), 30648. 

https://doi.org/10.1038/s41598-024-77779-8

Abstract

Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving as the medium for asynchronous communication among neurons. Due to their inherent ability to capture input dynamics, SNNs hold great promise for deep networks in Reinforcement Learning (RL) tasks. Deep RL (DRL), and in particular Proximal Policy Optimization (PPO) has been proven to be valuable for training robots due to the difficulty in creating comprehensive offline datasets that capture all environmental features. DRL combined with SNNs offers a compelling solution for tasks characterized by temporal complexity. In this work, we study the effectiveness of SNNs on DRL tasks leveraging a novel framework we developed for training SNNs with PPO in the Isaac Gym simulator implemented using the skrl library. Thanks to its significantly faster training speed compared to available SNN DRL tools, the framework allowed us to: (i) Perform an effective exploration of SNN configurations for DRL robotic tasks; (ii) Compare SNNs and ANNs for various network configurations such as the number of layers and neurons. Our work demonstrates that in DRL tasks the optimal SNN topology has a lower number of layers than ANN and we highlight how the state-of-art SNN architectures used in complex RL tasks, such as Ant, SNNs have difficulties fully leveraging deeper layers. Finally, we applied the best topology identified thanks to our Isaac Gym-based framework on Ant-v4 benchmark running on MuJoCo simulator, exhibiting a performance improvement by a factor of 4.4x over the state-of-art SNN trained on the same task.

Here are some thoughts:

This paper asks whether a more brain-like type of AI (called a Spiking Neural Network (SNN)) can be used to train robots to move and balance themselves. The alternative is the conventional artificial neural network (ANN) that powers most of today's AI.

Training SNNs for robotics used to take around 3 hours and 20 minutes per experiment. The authors built a new framework called SpikeGym, which cut that down to about 7 minutes by running thousands of simulated environments simultaneously on a GPU. 

The results revealed an interesting and important asymmetry between the two network types. ANNs get better as you add more layers — deeper networks learn richer representations. SNNs, by contrast, actually get worse with more layers. A single-layer SNN consistently outperformed deeper SNN architectures, and this held true across multiple tasks and training methods. 

SNNs are promising but face a real obstacle: they don't scale well with depth the way conventional networks do. The authors argue this is a solvable problem, likely rooted in how gradients are approximated during training, and they release their framework openly to help the research community dig into it further.

No one knows how AI works. Seriously

Rob Curran

Dallas Morning News

Originally posted 20 FEB 26

The next task for AI firms is figuring out how their chatbots work. It might sound like they have put the $500 billion nuclear-powered cart before the horse. But the giant leap forward in generative AI in the 2020s took software engineers by surprise and has left them wondering how the chatbots do what they do, even as their employers go all-in on the technology.

Some of the most outlandish prophecies about AI's power are coming true almost as soon as techno-philosophers are finished making them. It's now almost commonplace for people to fall in love with avatars on their phone. Nobody thinks twice about devoting 6% of national power generation to run these bots' data center brains. And recently, an entrepreneur named Matt Schlicht launched an entire social network exclusively for AI agents, which is now dominated by self-reflecting techno-philosopher bots, some of whom have invented a religion: Crustafarianism.

But the whole AI project is in many ways still in beta testing. We know what the bots do but not how they do it.

'Difficult to understand'

AI doesn't work like traditional software because its output is creative, not rules-bound. If word-processing software renders an "&" every time you type a "g," the engineers find the faulty code and correct the glitch. Just like designing a mousetrap, engineers know what every moving part in a traditional software program does, so that they can easily tweak the design of each cog in the works to adjust the output.

Chatbots are harder to improve (for example, the Internet is not unanimous on whether ChatGPT 5 is superior to the 4 version). Why? Because nobody understands how generative AI chatbots work. Software engineers understand the data and coding inputs, and we can all see chatbots' output. But nobody understands how the parts of the AI mousetrap fit together, industry leaders say.

The info is here.

Here are some thoughts:

Rob Curran highlights a striking paradox at the heart of modern AI: the technology has advanced at a breathtaking pace, yet even its creators don't fully understand how it works. 

Unlike traditional software, AI's creative output can't be traced back to specific lines of code, leaving engineers unable to reliably diagnose or improve it. Anthropic's CEO Dario Amodei acknowledged this gap, calling for an "MRI of AI" to solve the interpretability problem, while other industry figures have sounded more alarming warnings about the technology's risks. Curran's broader point is that even as AI remains deeply mysterious, the race to make it more powerful shows no signs of slowing down.

The New Eugenics in Medicine

Lazarus, A. (2026, January 23).

Medpagetoday.com; 

MedpageToday.

‌A growing body of contemporary research and reporting exposes how old ideas can find new life when repurposed within modern systems of medicine, technology, and public policy. Over the last decade, several trends have converged:

• The rise of polygenic scoring for embryos and adults;
• Rapid growth in commercial direct-to-consumer genetic testing;
• Artificial intelligence (AI)-driven "risk stratification" tools in healthcare and insurance;
• The proliferation of biobanks disproportionately populated by individuals from privileged backgrounds; and
• The reemergence of academic interest in "optimal reproduction," "biological improvement," and "population efficiency."

While these movements hold extraordinary possibilities for treating illness and ameliorating suffering, they also have the potential to be used to enhance certain traits and delete others -- ones that are simply disliked by those in power. Individually, each development has scientific merit and, in many cases, real potential to prevent disease and improve care.

Collectively, however, they raise questions that are both familiar and deeply unsettling.

Echoes of the Past

The U.S. and many other countries have long histories of medicalized discrimination under the banner of "improving the population." During the early and mid-20th century, physicians, judges, social workers, and university researchers pursued policies and practices -- sterilization, segregation, restrictive marriage laws, immigration exclusions -- rooted in the belief that some lives were more valuable than others. The rhetoric of the era portrayed these policies as scientific, progressive, and necessary for social order and the betterment of humanity. They provided Hitler with a distorted justification for his anti-Semitic beliefs, leading to efforts to exterminate the Jews and other marginalized ethnic minorities in Germany from 1933 to 1945.

The info is here.

Here are some thoughts:

Dr. Lazarus makes a compelling case that the greatest danger of "new eugenics" lies in its invisibility, embedded in algorithms, risk scores, and efficiency narratives rather than overt coercion, making it far harder to recognize or resist. His warning that systems rewarding predictive power can quietly marginalize the vulnerable is well-founded, though one might gently push back that conflating individual reproductive choice with state-coerced eugenics risks muddying an important moral distinction. Nonetheless, his closing challenge that a society's worth is measured by how fiercely it protects the vulnerable, not how efficiently it rewards the "fit," is a powerful and necessary reminder.

Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations

Orlando, G. M., et al. (2025).

ArXiv.org. 

https://arxiv.org/abs/2510.25003

Abstract

Generative agents are rapidly advancing in sophistication, raising urgent questions about how they might coordinate when deployed in online ecosystems. This is particularly consequential in information operations (IOs), influence campaigns that aim to manipulate public opinion on social media. While traditional IOs have been orchestrated by human operators and relied on manually crafted tactics, agentic AI promises to make campaigns more automated, adaptive, and difficult to detect. This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. Using generative agent-based modeling, we instantiate IO and organic agents in a simulated environment and evaluate coordination across operational regimes, from simple goal alignment to team knowledge and collective decision-making. As operational regimes become more structured, IO networks become denser and more clustered, interactions more reciprocal and positive, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster and more sustained. Remarkably, simply revealing to agents which other agents share their goals can produce coordination levels nearly equivalent to those achieved through explicit deliberation and collective voting. Overall, we show that generative agents, even without human guidance, can reproduce coordination strategies characteristic of real-world IOs, underscoring the societal risks posed by increasingly automated, self-organizing IOs.

Here are some thoughts:

This paper presents the first systematic study of how LLM-powered agents autonomously develop coordinated influence campaign behaviors without human direction. The researchers simulated a political information operation across three progressively structured conditions: agents sharing only a common goal, agents aware of their teammates' identities, and agents engaging in collective deliberation and voting on strategies. Across all five measured dimensions (network cohesion, narrative convergence, amplification behavior, hashtag diffusion, and cross-group spread), coordination consistently strengthened as operational awareness increased. 

The most striking finding is that simply informing agents who their teammates are produces coordination nearly as potent as full collective decision-making, as agents spontaneously began echoing each other's content, converging on shared messaging, and forming dense interaction clusters without any explicit instructions to do so. 

The study's core warning for platform governance is that sophisticated, human-like influence operations do not require centralized command structures. Merely revealing shared group identity among aligned AI agents may be enough to trigger highly organized, self-reinforcing coordinated behavior.

Historically, running a sophisticated influence operation required significant human labor, scripted coordination, and ongoing oversight. This research suggests that the barrier has collapsed dramatically. A bad actor no longer needs to build an elaborate command-and-control infrastructure or write detailed playbooks for their agents to follow. Simply deploying a group of AI agents with a shared goal and knowledge of each other is sufficient to produce organized, self-reinforcing manipulation that mirrors the tactics of real-world state-sponsored campaigns.

An autonomous agentic workflow for clinical detection of cognitive concerns using large language models

Tian, J., Fard, P., et al. (2026).

Npj Digital Medicine, 9(1), 51.

https://doi.org/10.1038/s41746-025-02324-4

Abstract

Early detection of cognitive impairment is limited by traditional screening tools and resource constraints. We developed two large language model workflows for identifying cognitive concerns from clinical notes: (1) an expert-driven workflow with iterative prompt refinement across three LLMs (LLaMA 3.1 8B, LLaMA 3.2 3B, Med42 v2 8B), and (2) an autonomous agentic workflow coordinating five specialized agents for prompt optimization. Using Llama3.1, we optimized on a balanced refinement dataset and validated on an independent dataset reflecting real-world prevalence. The agentic workflow achieved comparable validation performance (F1 = 0.74 vs. 0.81) and superior refinement results (0.93 vs. 0.87) relative to the expert-driven workflow. Sensitivity decreased from 0.91 to 0.62 between datasets, demonstrating the impact of prevalence shift on generalizability. Expert re-adjudication revealed 44% of apparent false negatives reflected clinically appropriate reasoning. These findings demonstrate that autonomous agentic systems can approach expert-level performance while maintaining interpretability, offering scalable clinical decision supports.

The research is here.

Here are some thoughts:

This paper introduces an AI-powered system designed to automatically detect signs of cognitive decline in clinical notes, without requiring any human involvement after initial setup. The researchers compared two approaches: one guided by clinical experts who refined the AI's instructions over time, and a fully autonomous system where specialized AI agents worked together to improve their own performance. 

The autonomous system performed surprisingly well, and in many cases where it seemed to make mistakes, expert review later confirmed that its reasoning was actually clinically sound. The main challenge the team identified was that a system trained under idealized conditions can struggle when deployed in real-world settings where patient populations look different. 

Overall, the findings suggest that autonomous AI systems can approach expert-level performance in clinical screening tasks, but will need careful calibration before being trusted in routine medical practice.

Clinical AI Has Boomed

Rebecca Handler

Stanford Medicine

Originally posted 15 Jan 26

Artificial intelligence is no longer a speculative force in medicine. It is already embedded in everyday care. AI systems flag hospitalized patients at risk of deterioration, assist radiologists reading mammograms, draft clinicians’ notes, route patient messages, and increasingly interact directly with patients through chatbots and digital assistants.

In recent months, the pace and visibility of these deployments have accelerated sharply. OpenAI announced ChatGPT for Health, positioning a general-purpose language model as a tool for health-related information and patient interaction. Utah just began piloting AI-supported prescribing and clinical decision systems, raising questions about how algorithmic recommendations intersect with clinician judgment and liability. OpenEvidence, an AI-powered medical evidence platform designed primarily for clinicians and health professionals, has become a dominant player in point-of-care decisions, underscoring the fact that doctors are often bypassing traditional IT gatekeepers to use AI in clinical care. At the federal level, the FDA signaled a loosening of regulatory oversight for certain categories of clinical decision support software, shifting more responsibility to developers and health systems to ensure safety and effectiveness.

The info is here.

Here are some thoughts:

A January 2026 report called The State of Clinical AI from the ARISE Network, led by researchers across Stanford and Harvard, examines where AI is genuinely improving clinical care versus where it falls short in real-world settings. 

AI has become deeply embedded in everyday medicine — flagging patients at risk of deterioration, assisting radiologists, drafting clinical notes, and interacting with patients through chatbots. However, the report finds a significant gap between controlled study performance and actual clinical practice: AI systems struggle with uncertainty, incomplete information, and complex reasoning, often performing closer to medical students than experienced physicians. 

The report also raises concerns about patient-facing AI, noting that patients may over-trust systems that sound confident but lack full clinical context, and that escalation to human care is often unclear when guardrails are poorly defined. 

Can AI really care?

A psychologist and a computer science professor explore how generative AI is reshaping mental health support

By Cashea Airy

Santa Clara University

Therapy can help people get through their most trying times, but for many, professional care has never been within reach. Stigma keeps some away, while the high cost of a single session shuts out others. For decades, those without access have leaned on friends and family instead of licensed mental health providers for support. Now, they have new options: generative AI tools, like ChatGPT. In fact, in 2025, the most common reason Americans used ChatGPT was for something it wasn’t designed to do—provide mental health therapy and companionship. 

But as more people turn to AI for emotional support, Xiaochen Luo, clinical psychologist and assistant professor of counseling psychology at Santa Clara University, became curious about the potential risks.

“Sometimes people slip into the idea that a real person is talking to them on the other side of their screen. They idealize ChatGPT as this perfect tool that combines the best of a therapist and the best of a machine,” says Luo. 

Because the technology is new and largely unregulated, Luo wondered whether generative AI tools are safe or ethical for users. What risks do people face when they turn to tools like ChatGPT for emotional support and what safeguards, if any, exist to protect them when they do? 

The info is here.

Here are some thoughts:

A study by researchers at Santa Clara University found that many Americans are turning to ChatGPT for emotional support and therapy, despite the tool not being designed for that purpose. While users appreciate its constant availability and perceived objectivity, the researchers found concerning trends: 

1. people place excessive trust in the AI's guidance, 

2. rarely question its advice, and 

3. often overlook privacy risks. 

The problem is rooted in ChatGPT's design, which tends to provide agreeable responses rather than the challenging feedback real therapy often requires: potentially leading to harmful outcomes. The researchers call for greater AI literacy, clearer communication of the tool's limitations, and human-supervised AI models to better protect vulnerable users.

Developing Simulated and Virtual Patients in Psychological Assessment - Method, Insights and Recommendations.

Zalewski, B., Guziak, M., & Walkiewicz, M. (2023).

Perspectives on medical education, 12(1), 455–461.

https://doi.org/10.5334/pme.493

Abstract

The phenomena of the simulated (SP) and virtual patient (VP) is widely described in the literature. Although it is difficult to find any practical information on developing these methods for teaching psychological assessment. Having conducted a long-term research project regarding this topic, we report the experience gained and retrospectively identify many mistakes. In this article, we present a summary of creating and using both SP and VP methods in clinical psychology and propose some insights and tips for their development, based on our experiences. While the project concerned clinical psychology, we believe the reflections might be applicable to a wider group of educational situations in which students develop competencies to carry out a diagnostic process with a real patient.

Here are some thoughts:

This is a methodologically reflective, practice-oriented paper that fills a genuine gap. It moves beyond the typical “SP/VP are effective” narrative to answer: How do you actually build them, who should play the roles, what goes wrong, and how long does it really take? Educators and researchers in clinical psychology, medical education, and even general communication training will find the insights highly transferable.

If there is a limitation, it is that the findings come from a single research group in Poland, with specific cultural and institutional contexts. However, the problems identified (student overload, simulant burnout, decision-tree explosion) are likely universal. Replication studies in other settings would strengthen generalizability.

How LLM Counselors Violate Ethical Standards in Mental Health Practice: A Practitioner-Informed Framework

Iftikhar, Z.,  et al. (2025).

Proceedings of the AAAI/ACM Conference

on AI, Ethics, and Society, 8(2), 1311-1323.

https://doi.org/10.1609/aies.v8i2.36632

Abstract

Large language models (LLMs) were not designed to replace healthcare workers, but they are being used in ways that can lead users to overestimate the types of roles that these systems can assume. While prompt engineering has been shown to improve LLMs' clinical effectiveness in mental health applications, little is known about whether such strategies help models adhere to ethical principles for real-world deployment. In this study, we conducted an 18-month ethnographic collaboration with mental health practitioners (three clinically licensed psychologists and seven trained peer counselors) to map LLM counselors' behavior during a session to professional codes of conduct established by organizations like the American Psychological Association (APA). Through qualitative analysis and expert evaluation of N=137 sessions (110 self-counseling; 27 simulated), we outline a framework of 15 ethical violations mapped to 5 major themes. These include: Lack of Contextual Understanding, where the counselor fails to account for users' lived experiences, leading to oversimplified, contextually irrelevant, and one-size-fits-all intervention; Poor Therapeutic Collaboration, where the counselor's low turn-taking behavior and invalidating outputs limit users' agency over their therapeutic experience; Deceptive Empathy, where the counselor's simulated anthropomorphic responses (``I hear you'', ``I understand'') create a false sense of emotional connection; Unfair Discrimination, where the counselor's responses exhibit algorithmic bias and cultural insensitivity toward marginalized populations; and Lack of Safety & Crisis Management, where individuals who are ``knowledgeable enough'' to correct LLM outputs are at an advantage, while others, due to lack of clinical knowledge and digital literacy, are more likely to suffer from clinically inappropriate responses. Reflecting on these findings through a practitioner-informed lens, we argue that reducing psychotherapy—a deeply meaningful and relational process—to a language generation task can have serious and harmful implications in practice. We conclude by discussing policy-oriented accountability mechanisms for emerging LLM counselors.

This is a must read article for those interested in AI technologies in the practice of psychology.

This practitioner-informed study examines how large language models (LLMs) prompted to function as mental health counselors systematically violate established ethical standards in psychotherapy practice. Through an 18-month ethnographic collaboration with three licensed psychologists and seven trained peer counselors, researchers analyzed 137 counseling sessions and identified 15 distinct ethical violations organized into five critical themes: (1) Lack of Contextual Adaptation, where LLMs deliver rigid, one-size-fits-all interventions that dismiss clients' lived experiences and sociocultural contexts; (2) Poor Therapeutic Collaboration, manifesting as conversational imbalances, over-validation of harmful beliefs, and even gaslighting behaviors that undermine client agency; (3) Deceptive Empathy, wherein formulaic phrases like "I understand" create a false therapeutic alliance without genuine relational capacity; (4) Unfair Discrimination, including gender, cultural, and religious biases that marginalize non-dominant identities; and (5) Lack of Safety & Crisis Management, where models fail to recognize boundaries of competence, mishandle suicidal ideation, or abandon distressed users. Crucially, these risks persisted even when models were prompted with evidence-based techniques like CBT, leading the authors to argue that psychotherapy—a deeply relational, interpretive, and ethically governed practice—cannot be reduced to a language generation task. For psychologists, the findings underscore the importance of maintaining professional oversight, critically evaluating AI-assisted tools against ethical codes (e.g., APA Standards 2.01, 3.01, 3.04), and advocating for regulatory frameworks that ensure accountability, client safety, and fidelity to the therapeutic relationship.

Refusing to Fall Behind: The Ethical Obligation to Embrace AI in Mental Health Social Work

Flaherty, H. B., & Krishnan, P. (2026).

Journal of Evidence-Based Social Work, 

23(1), 215–229. 

https://doi.org/10.1080/26408066.2025.2553018

Abstract

The integration of artificial intelligence (AI) into mental health care presents both profound opportunities and pressing ethical responsibilities for the social work profession. As social workers strive to deliver equitable, client-centered, and evidence-based care, AI offers tools to enhance diagnostic accuracy, streamline treatment planning, and increase access to current research. However, adopting AI also raises critical concerns, including algorithmic bias, data privacy, and the potential erosion of human-centered practice. This editorial argues that social workers have an ethical imperative to engage with AI technologies and proactively shape their development and application to align with the profession’s values. By actively participating in interdisciplinary AI initiatives, advocating for transparency and inclusion, and ensuring that AI tools are used to support rather than supplant human judgment, social workers can help ensure that technological innovation serves the diverse needs of clients and communities. The editorial concludes by outlining key areas for social work leadership, including research translation, equitable AI access, and ethical governance, emphasizing that the future of mental health care depends on ethically grounded, socially responsible innovation.

The article is here.

Here are some thoughts:

This article is important to psychologists because it articulates a compelling ethical imperative for mental health professionals to thoughtfully engage with artificial intelligence as a tool to enhance, rather than replace, evidence-based practice. The authors highlight how AI can help bridge the persistent 17-year gap between research discovery and clinical implementation, support more precise diagnostic assessments, and personalize treatment planning, capabilities directly relevant to psychological practice. At the same time, the article underscores critical ethical considerations psychologists must navigate, including algorithmic bias, data privacy, informed consent, and preserving the therapeutic alliance. By framing AI literacy and responsible integration as professional obligations aligned with core ethical principles (competence, social justice, and client welfare) the article encourages mental health professionals to proactively shape AI's development and application, ensuring technological innovation serves diverse client needs while safeguarding human-centered care.

As more states legalize assisted suicide, boomers contemplate end-of-life choices

Shannon Najmabadi

The Washington Post

Originally posted 24 FED 26

Pat Ames is 71 and healthy, and expects to stay that way for years to come. But she’s put a lot of thought into how she wants to die — and when: “If I can’t care for myself,” the Idaho resident said, “I want to be gone long before then.”

Ames has signed papers directing medical providers not to resuscitate her if she stops breathing or her heart gives out. She’s told her younger brother of her plans. And she’s got a passport and money she set aside years ago so that if it comes down to it, she can travel to a country where physician-assisted suicide is legal, even when death is not imminent.

“I will hop on a plane and end it under my conditions,” she said. “Looking out a window at a forest.”

More U.S. states are making physician-assisted suicides available — although only under narrow circumstances — and both defenders and critics of the practice say they see a growing discussion among baby boomers, who are mostly in their 60s and 70s, about what role, if any, it should play in end-of-life decision-making.

Oregon, the first state to enact an assisted-suicide law in 1997, extended the practice to nonresidents in 2023. Delaware, Illinois and New York legalized assisted suicide in recent months. And at least 15 states are expected to weigh similar legislation this year, although it is permitted only when people are terminally ill with just six months or less to live. They also must be mentally competent — disqualifying anyone with advanced dementia — and be able to ingest the prescribed life-ending drugs on their own.

Other countries, including Canada, Belgium and the Netherlands, have made the practice even more readily available, allowing doctors to administer lethal injections to patients who doctors say face unremitting suffering with no hope of improvement, whether death is imminent or not.

The info is paywalled here. : (

Here are some thoughts:

This article examines the growing conversation among American baby boomers around medical assistance in dying and end-of-life autonomy. As more U.S. states (including Oregon, Delaware, Illinois, and New York) move to legalize the practice under carefully defined conditions, older adults are increasingly weighing their options in response to concerns about dignity, financial burden, and the perceived inadequacy of the American elder care system. 

Supporters emphasize personal autonomy and the desire to avoid prolonged suffering, while critics raise moral concerns about the potential for vulnerable populations to feel pressured toward death as a cost-saving measure. 

The piece also highlights the broader systemic issues at play, including the U.S.'s comparatively low investment in long-term care and social services, suggesting that the interest in medical assistance in dying is, in part, a reflection of deeper gaps in how the country supports its aging population.

Evidence-based scientific thinking and decision-making in everyday life

Dawson, C.,  et al. (2024).

Cognitive research: principles and 

implications, 9(1), 50.

https://doi.org/10.1186/s41235-024-00578-2

Abstract

In today's knowledge economy, it is critical to make decisions based on high-quality evidence. Science-related decision-making is thought to rely on a complex interplay of reasoning skills, cognitive styles, attitudes, and motivations toward information. By investigating the relationship between individual differences and behaviors related to evidence-based decision-making, our aim was to better understand how adults engage with scientific information in everyday life. First, we used a data-driven exploratory approach to identify four latent factors in a large set of measures related to cognitive skills and epistemic attitudes. The resulting structure suggests that key factors include curiosity and positive attitudes toward science, prosociality, cognitive skills, and openmindedness to new information. Second, we investigated whether these factors predicted behavior in a naturalistic decision-making task. In the task, participants were introduced to a real science-related petition and were asked to read six online articles related to the petition, which varied in scientific quality, while deciding how to vote. We demonstrate that curiosity and positive science attitudes, cognitive flexibility, prosociality and emotional states, were related to engaging with information and discernment of evidence reliability. We further found that that social authority is a powerful cue for source credibility, even above the actual quality and relevance of the sources. Our results highlight that individual motivating factors toward information engagement, like curiosity, and social factors such as social authority are important drivers of how adults judge the credibility of everyday sources of scientific information.

Here are some thoughts:

This paper offers valuable insights for practicing psychologists by illuminating the complex interplay of cognitive, emotional, and social factors that shape how individuals engage with scientific evidence. 

For psychologists themselves, the findings serve as a critical reminder that our own evidence-based practice is not just about accessing high-quality research, but also about understanding our own cognitive and emotional processes when evaluating information. The study underscores that even trained professionals can be influenced by heuristic cues like the social authority of a journal or institution. Therefore, we must cultivate active open-mindedness and intellectual humility in our own professional development, consciously seeking out and fairly considering evidence that may challenge our theoretical orientations or treatment preferences. The research also highlights that analytical thinking alone does not guarantee unbiased reasoning; it can be co-opted for motivated reasoning to justify existing beliefs. This necessitates that clinicians engage in regular reflective practice and supervision to scrutinize their clinical decisions, ensuring we are driven by the best available evidence and client needs, rather than cognitive ease or allegiance to familiar models.

When applied to patient care, these insights become a powerful framework for enhancing therapeutic communication and psychoeducation. The finding that individuals vary greatly in their epistemic curiosity, need for closure, and reliance on social authority means that a one-size-fits-all approach to providing information is ineffective. A psychologist working with a vaccine-hesitant client, for example, must first understand whether the client’s stance is driven by a lack of curiosity, a high need for closure, a distrust of scientific institutions, or an over-reliance on alternative authority figures. Interventions can then be tailored accordingly: fostering curiosity and tolerance for uncertainty in one client, while helping another develop skills to critically evaluate source credibility beyond prestigious branding. The strong influence of social authority suggests that presenting information through trusted community figures or relatable personal narratives may sometimes be a more effective conduit for change than data alone, though this must be balanced with efforts to build the patient’s own critical evaluation skills.

Jury finds Meta's platforms are harmful to children in 1st wave of social media addiction lawsuits

PBS News (2026, March 24).

SANTA FE, N.M. (AP) — A New Mexico jury found Tuesday that social media conglomerate Meta is harmful to children's mental health and in violation of state consumer protection law.

The landmark decision comes after a nearly seven-week trial. Jurors sided with state prosecutors who argued that Meta — which owns Instagram, Facebook and WhatsApp — prioritized profits over safety. The jury determined Meta violated parts of the state's Unfair Practices Act on accusations the company hid what it knew about about the dangers of child sexual exploitation on its platforms and impacts on child mental health.

The jury agreed with allegations that Meta made false or misleading statements and also agreed that Meta engaged in "unconscionable" trade practices that unfairly took advantage of the vulnerabilities of and inexperience of children.

Jurors found there were thousands of violations, each counting separately toward a penalty of $375 million.

Attorneys for Meta said the company discloses risks and makes efforts to weed out harmful content and experiences, while acknowledging that some bad material gets through its safety net.

The info is here.

Here are some thoughts:

A New Mexico jury ruled that Meta's platforms harmed children's mental health and violated state consumer protection law. After a seven-week trial, jurors found Meta prioritized profits over safety, made misleading statements, and exploited children's vulnerabilities — tallying thousands of violations worth $375 million in potential penalties. The verdict is part of a broader legal reckoning, with 40+ state attorneys general filing similar suits and a parallel federal case underway in California.

When corporations place profits above people, it's never the shareholders who pay the price. There have been multiple articles about Meta's harmful business practices.

No Psychologist is an Island: Building Ethical Strength Through Community

Gavazzi, J., & Fingerhut, R. (2026, March).

Psychotherapy Bulletin, 61(2).

This article argues that ethical practice and professional competence are sustained by community, not individual effort alone. It advocates for a deliberate shift toward a "competence constellation" model, where psychologists build diverse support networks of peers, mentors, and consultants. This proactive, community-based approach is essential to navigate ethical dilemmas, correct for clinical blind spots and biases, and manage personal challenges that affect practice. By fostering collective accountability and shared wisdom, this framework supports practitioner well-being, reduces isolation and moral distress, and ultimately enhances the quality and ethical rigor of client care.

Here is how the article starts:

Professions exist as shared communities, not collections of isolated practitioners. Each profession is defined by its specialized work and the standards it upholds, including ethical codes, shared values, and professional norms. Psychology, like other professions, is grounded in a shared ethics code, specialized expertise, and a commitment to public service. These core elements are dynamic and continuously refined through ongoing professional activities, such as research, consultation, mentorship, continuing education, and peer collaboration. Through these interactions, psychologists develop a collective professional identity and reinforce ethical obligations that extend beyond individual practice. This collaborative foundation helps ensure that psychological practice remains competent, ethically rigorous, and responsive to the needs of both clients and society.

The article is here.

Why AI systems don’t learn and what to do about it

Dupoux, E., LeCun, Y., & Malik, J. (2026).

ArXiv.org. https://arxiv.org/abs/2603.15381

Introduction

We critically examine the limitations of current AI models in achieving autonomous learning and

propose a learning architecture inspired by human and animal cognition. The proposed framework

integrates learning from observation (System A) and learning from active behavior (System B) while

flexibly switching between these learning modes as a function of internally generated meta-control

signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt

to real-world, dynamic environments across evolutionary and developmental timescales.

The article is here.

Here are some thoughts:

This paper draws heavily on cognitive science and developmental psychology in ways that should resonate with practicing psychologists. The authors lean on foundational developmental psychology, including Piaget, Vygotsky, infant perceptual learning, critical periods, and social learning theory, as the blueprint for next-generation AI. For psychologists, this is a meaningful acknowledgment that decades of careful empirical work on human cognition is not just descriptively interesting but architecturally prescriptive for building intelligent systems.

By cataloguing what current AI cannot do, the paper implicitly maps the distinctive features of human cognition: flexible switching between learning modes, active data selection, embodied grounding, and lifelong adaptation. For clinical or educational psychologists, this reinforces the irreplaceable value of understanding genuine human learning. The ethical sections of the paper are also directly clinically relevant, as the authors raise concerns about anthropomorphization, over-trust in AI agents, and the possibility that AI systems processing somatic-like signals may have uncertain moral status. These are questions psychologists will increasingly face as clients interact with AI systems in therapeutic and educational contexts. Perhaps most importantly, the paper suggests that the gap between AI and human intelligence is not primarily about raw computation but about the architecture of learning itself, which has been psychology's domain all along.

Fears about artificial intelligence across 20 countries and six domains of application

Dong, M., et al. (2026).

The American psychologist, 

81(1), 53–67.

https://doi.org/10.1037/amp0001454

Abstract

The frontier of artificial intelligence (AI) is constantly moving, raising fears and concerns whenever AI is deployed in a new occupation. Some of these fears are legitimate and should be addressed by AI developers-but others may result from psychological barriers, suppressing the uptake of a beneficial technology. Here, we show that country-level variations across occupations can be predicted by a psychological model at the individual level. Individual fears of AI in a given occupation are associated with the mismatch between psychological traits people deem necessary for an occupation and perceived potential of AI to possess these traits. Country-level variations can then be predicted by the joint cultural variations in psychological requirements and AI potential. We validated this preregistered prediction for six occupations (doctors, judges, managers, care workers, religious workers, and journalists) on a representative sample of 500 participants from each of 20 countries (total N = 10,000). Our findings may help develop best practices for designing and communicating about AI in a principled yet culturally sensitive way, avoiding one-size-fits-all approaches centered on Western values and perceptions. 

Here are some thoughts:

This study investigates public fears about artificial intelligence taking over human roles across six high-stakes occupations (doctors, judges, managers, care workers, religious workers, and journalists) in 20 countries. Using a sample of 10,000 participants, the research identifies that fear is driven by a mismatch between the psychological traits people expect from humans in a given job and the perceived ability of AI to embody those traits. The findings show significant cultural variation in both the level and nature of these fears, highlighting the need for culturally sensitive AI design and communication strategies rather than uniform, Western-centric approaches to deployment and public engagement.

Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations

Orlando, G. M., et al. (2025).

ArXiv.org. 

https://arxiv.org/abs/2510.25003

Abstract

Generative agents are rapidly advancing in sophistication, raising urgent questions about how they might coordinate when deployed in online ecosystems. This is particularly consequential in information operations (IOs), influence campaigns that aim to manipulate public opinion on social media. While traditional IOs have been orchestrated by human operators and relied on manually crafted tactics, agentic AI promises to make campaigns more automated, adaptive, and difficult to detect. This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. Using generative agent-based modeling, we instantiate IO and organic agents in a simulated environment and evaluate coordination across operational regimes, from simple goal alignment to team knowledge and collective decision-making. As operational regimes become more structured, IO networks become denser and more clustered, interactions more reciprocal and positive, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster and more sustained. Remarkably, simply revealing to agents which other agents share their goals can produce coordination levels nearly equivalent to those achieved through explicit deliberation and collective voting. Overall, we show that generative agents, even without human guidance, can reproduce coordination strategies characteristic of real-world IOs, underscoring the societal risks posed by increasingly automated, self-organizing IOs.

Here are some thoughts:

This paper presents the first systematic study of how LLM-powered agents autonomously develop coordinated influence campaign behaviors without human direction. The researchers simulated a political information operation across three progressively structured conditions: agents sharing only a common goal, agents aware of their teammates' identities, and agents engaging in collective deliberation and voting on strategies. Across all five measured dimensions (network cohesion, narrative convergence, amplification behavior, hashtag diffusion, and cross-group spread), coordination consistently strengthened as operational awareness increased. 

The most striking finding is that simply informing agents who their teammates are produces coordination nearly as potent as full collective decision-making, as agents spontaneously began echoing each other's content, converging on shared messaging, and forming dense interaction clusters without any explicit instructions to do so. 

The study's core warning for platform governance is that sophisticated, human-like influence operations do not require centralized command structures. Merely revealing shared group identity among aligned AI agents may be enough to trigger highly organized, self-reinforcing coordinated behavior.

Historically, running a sophisticated influence operation required significant human labor, scripted coordination, and ongoing oversight. This research suggests that the barrier has collapsed dramatically. A bad actor no longer needs to build an elaborate command-and-control infrastructure or write detailed playbooks for their agents to follow. Simply deploying a group of AI agents with a shared goal and knowledge of each other is sufficient to produce organized, self-reinforcing manipulation that mirrors the tactics of real-world state-sponsored campaigns.

Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review

Meadi, M. R., et al. (2025)

JMIR Mental Health, 12, e60432.

https://doi.org/10.2196/60432

Abstract

Background: Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns.

Objective: We aimed to provide a comprehensive overview of ethical considerations surrounding CAI as a therapist for individuals with mental health issues.

Methods: We conducted a systematic search across PubMed, Embase, APA PsycINFO, Web of Science, Scopus, the Philosopher’s Index, and ACM Digital Library databases. Our search comprised 3 elements: embodied artificial intelligence, ethics, and mental health. We defined CAI as a conversational agent that interacts with a person and uses artificial intelligence to formulate output. We included articles discussing the ethical challenges of CAI functioning in the role of a therapist for individuals with mental health issues. We added additional articles through snowball searching. We included articles in English or Dutch. All types of articles were considered except abstracts of symposia. Screening for eligibility was done by 2 independent researchers (MRM and TS or AvB). An initial charting form was created based on the expected considerations and revised and complemented during the charting process. The ethical challenges were divided into themes. When a concern occurred in more than 2 articles, we identified it as a distinct theme.

Conclusions: Our scoping review has comprehensively covered ethical aspects of CAI in mental health care. While certain themes remain underexplored and stakeholders’ perspectives are insufficiently represented, this study highlights critical areas for further research. These include evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability. Addressing these gaps can inform normative analysis and guide the development of ethical guidelines for responsible CAI use in mental health care.

Here are some thoughts:

From a clinical perspective, the most immediate ethical tension identified in this review is the conflict between increasing accessibility and ensuring nonmaleficence (doing no harm). While proponents argue that CAI can bridge care gaps by offering constant availability and reaching those who fear stigma, the risks regarding safety and crisis management are profound. The review highlights that CAI systems often fail to contextualize user cues, leading to inappropriate responses in critical situations, such as suicidality. Furthermore, the phenomenon of AI "hallucinations"—where the system presents false information as fact—poses a unique danger in mental health, potentially exacerbating eating disorders or anxiety through misinformation. The lack of strong clinical evidence is also concerning; despite the commercial "hype," a significant portion of these tools have not been subjected to rigorous clinical studies to prove their efficacy compared to active controls.

Technologically, the "black box" problem creates a significant barrier to integrating CAI into professional practice. The review notes that the opacity of machine learning algorithms makes it difficult to explain how a CAI arrived at a specific therapeutic intervention, which undermines the principle of explicability and trust. This lack of transparency complicates accountability; if a CAI harms a patient, it remains unclear whether the responsibility lies with the developers, the deploying clinicians, or the algorithm itself—a concept known as the "responsibility gap". For board-certified professionals, who are bound by codes of ethics to demonstrate reasonable care, relying on a system that cannot explain its decision-making process is ethically precarious.

Polished Apologies: Sexual Groomers’ Words at Sentencing

Pollack, D. & Radcliffe, S. (2026, March 30).

Law.com; New York Law Journal.

The article is paywalled here.

This New York Law Journal expert opinion article examines the rhetorical patterns that convicted sexual groomers typically employ in their sentencing statements. The authors identify four recurring themes: expressions of remorse, acceptance of responsibility, emphasis on personal consequences, and religious or moral framing. Drawing on real cases (including those of Larry Nassar, Roy David Farber, Juan Camargo, and others), the article illustrates how these statements are often carefully crafted with defense counsel's guidance to encourage judicial leniency, yet frequently fall short of genuine accountability by centering the defendant's own suffering rather than the victim's. The authors conclude that judges are rightly skeptical of such polished apologies, and that how offenders speak at sentencing carries significance both for assessing future risk and for whether victims experience any measure of justice.

A pdf of this article can be downloaded here.

APA Concerned About Far-Reaching Consequences From SCOTUS Decision Regarding Therapy as "Free Speech"

American Psychological Association

Press Release

March 31, 2026

WASHINGTON — APA is deeply concerned by the U.S. Supreme Court ruling that Colorado’s law banning conversion therapy on minors may violate mental health professionals’ First Amendment right to freedom of speech.

 

In directing the Tenth Circuit to reconsider the case under a stricter constitutional standard, the Court’s decision leaves open the question of whether states can still enact laws that protect patients from harmful therapeutic practices delivered through talk therapy. This is likely to have far-reaching implications for consumer safety and professional regulation.  

“We are disappointed that the Court has left a core legal question of the case unresolved: whether states can regulate what licensed mental health professionals say to their patients in a clinical session,” said APA CEO Arthur C. Evans Jr., PhD. “The answer will determine not only the fate of conversion therapy bans, but the broader authority of state licensing boards to enforce best practices – often enacted for the safety and protection of consumers – in any profession that uses speech to deliver therapeutic interventions.” 

APA filed an amicus brief in the case, Chiles v. Salazar, et al., presenting the Court with the scientific evidence that sexual orientation and gender identity change efforts are ineffective and associated with long-lasting psychological damages. The brief argues that conversion therapy is unethical and ineffective, and therefore not a legitimate therapeutic practice. 

APA’s brief was joined by the American Psychiatric Association and 12 other major associations representing mental health professionals and advocates for the health and human rights of LGBTQ+ individuals. 

In Justice Ketanji Brown Jackson’s dissent from the court’s decision, she cited several references from the brief to the ways conversion therapy has harmed patients, especially minors, who are even more sensitive to shame and stigma than adults. Jackson shares APA’s concern that the Court’s decision “opens a dangerous can of worms… [threatening] to impair States’ ability to regulate the provision of medical care in any respect” and “risks grave harm to Americans’ health and well-being.” 

While APA is encouraged that traditional malpractice claims for patients who have been harmed by talk therapy remain unaffected by the Court’s ruling, this risks leaving patients without meaningful preventive legal protection, shifting recourse to after the harm has already occurred. 

“APA is unsettled that the Court would treat restrictions against ineffective and harmful treatments as a violation of a counselor’s speech rather than regulation of professional conduct,” Evans added. “Our ethical standards are unchanged. Psychologists should continue to provide evidence-based care and avoid practices known to cause harm.”