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.