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.

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.

Artificial research participants in behavioral science

Medina, V. A., & Mohan, M. (2025).

Journal of Ethics in Entrepreneurship

and Technology, 1–10.

https://doi.org/10.1108/jeet-03-2025-0014

Purpose

The potential for large language models (LLMs) to improve behavioral science research has generated significant discussion. But, the specific role that LLMs should serve in behavioral research, especially in terms of simulating human participants, remains an open research question. The purpose of this work is to engage with this open question and address a critical gap in the literature stemming from the lack of a practical framework for realistically using artificial research participants.

Design/methodology/approach

Google Scholar was systematically searched for modern, peer-reviewed literature. Additional articles were found by both backward and forward citation searching the relevant articles. Exclusion criteria were articles that were not directly related to artificial intelligence (AI) and/or research participants, and articles written in a language other than English. This approach resulted in 26 citations that comprehensively capture current perspectives.

Findings

This study proposes two novel stances: that artificial research participants can complement human participants during data collection, and replace human participants during pilot testing. This framework engages with the open question of artificial research participants usage while addressing a framework gap in the literature.

Originality/value

This workadvances discourse LLMs potentially transforming behavioral science by establishing a framework differentiating the use of artificial research participants in data collection versus pilot testing. This study reinforces this framework with clear implementation guidelines that maximize the strengths of AI while respecting the human element and the methodological integrity of behavioral research.

Here are some thoughts:

This paper matters for practicing psychologists because it signals a meaningful and near-term shift in how behavioral research will be conducted (which directly affects the evidence base clinicians rely on). For those who conduct or supervise research, it offers the first concrete guidance on a question that has been debated without resolution: LLMs aren't ready to replace human participants in full data collection, but they may already be capable of improving pilot testing and serving as a useful check on the robustness of findings. Used carefully, transparently, and with an awareness of their limitations (particularly their tendency to flatten human variability on ambiguous topics like morality), artificial research participants represent a practical efficiency gain, especially for researchers working with limited participant pools or tight budgets. Staying informed about this framework now puts psychologists in a better position to critically evaluate the research they read, ask good questions about how studies were conducted, and make thoughtful decisions about whether and how to incorporate these tools into their own work.

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Defining intelligence: Bridging the gap between human and artificial perspectives

Gignac, G. E., & Szodorai, E. T. (2024).

Intelligence, 104, 101832–101832.

https://doi.org/10.1016/j.intell.2024.101832

Abstract

Achieving a widely accepted definition of human intelligence has been challenging, a situation mirrored by the diverse definitions of artificial intelligence in computer science. By critically examining published definitions, highlighting both consistencies and inconsistencies, this paper proposes a refined nomenclature that harmonizes conceptualizations across the two disciplines. Abstract and operational definitions for human and artificial intelligence are proposed that emphasize maximal capacity for completing novel goals successfully through respective perceptual-cognitive and computational processes. Additionally, support for considering intelligence, both human and artificial, as consistent with a multidimensional model of capabilities is provided. The implications of current practices in artificial intelligence training and testing are also described, as they can be expected to lead to artificial achievement or expertise rather than artificial intelligence. Paralleling psychometrics, ‘AI metrics’ is suggested as a needed computer science discipline that acknowledges the importance of test reliability and validity, as well as standardized measurement procedures in artificial system evaluations. Drawing parallels with human general intelligence, artificial general intelligence (AGI) is described as a reflection of the shared variance in artificial system performances. We conclude that current evidence more greatly supports the observation of artificial achievement and expertise over artificial intelligence. However, interdisciplinary collaborations, based on common understandings of the nature of intelligence, as well as sound measurement practices, could facilitate scientific. 

Highlights

• Proposes unified definitions for human and artificial intelligence.

• Distinguishes between artificial achievement/expertise and artificial intelligence.

• Advocates for AI metrics to ensure good quality AI system evaluations.

• Describes artificial general intelligence (AGI) mirroring human general intelligence.

• Evidence currently favours presence of artificial achievement over intelligence.

Here are some thoughts:

This paper is critical in the context of rapid AI acceleration because it establishes a rigorous, interdisciplinary nomenclature to distinguish genuine "artificial intelligence" from what the authors term "artificial achievement" or "expertise". While modern AI developments often focus on the impressive performance of systems on specific benchmarks, this paper highlights that these systems are frequently trained on the very test items used to evaluate them, which violates the fundamental psychological requirement of "novelty" for measuring intelligence. By proposing a harmonized "AI metrics" framework that parallels psychometrics, the authors provide a scientific basis for evaluating whether AI systems possess a multidimensional capacity to solve unpracticed, novel goals rather than simply reflecting extensive data processing and pattern recognition from their training sets. Ultimately, the paper warns that current acceleration may be producing highly specialized expertise rather than true artificial general intelligence (AGI), and it suggests that scientific progress requires moving beyond single-dimension metrics to embrace more complex, hierarchical models of capability.

From Clinical Judgment to Machine Learning: Rethinking Psychotherapeutic Decision-Making with Artificial Intelligence

Onah, C., & Gwar, N. (2025).

Psychotherapy Bulletin, 60(4), 45-54.

According to the World Health Organization (WHO; n.d.) mental health disorders, such as anxiety disorder, bipolar disorder, schizophrenia and post-traumatic stress disorder (PTSD), are some of the most significant public health challenges in the WHO European Region. Within this region (which includes 53 countries across Europe and parts of Central Asia), mental health disorders are the leading cause of disability and the third leading cause of overall disease burden. Among these disorders, depression remains one of the most common mental illnesses globally, yet a staggering 66% of affected individuals continue to live with unmet treatment needs (Eilert et al., 2021; World Health Organization, 2023).

Empirically supported psychotherapeutic treatments have demonstrated strong efficacy, are endorsed by clinical guidelines, and are widely used in mental health care as a preferred first-line treatment option (Lorimer et al., 2021). A substantial body of research and numerous clinical trials have affirmed their effectiveness across a wide spectrum of mental and behavioral health disorders (Eilert et al., 2021), applicable to diverse settings (e.g., primary care medicine, community health, specialty treatment services), and across the lifespan (Nathan & Gorman, 2007). In addition, psychotherapy research has identified both specific and non-specific factors that contribute to treatment outcomes (Norcross & Lambert, 2019). Beyond core techniques and strategies, broader factors, such as the quality of the therapeutic relationship, therapist competence, and adherence to protocols, significantly shape psychotherapy’s clinical effectiveness. As such, psychotherapy is a fundamentally human-centered practice, dependent on the dynamic interplay of targeted interventions delivered within a professional, relational framework to effect meaningful clinical change (Aafjes-van Doorn et al., 2020).

At the same time, artificial intelligence (AI) and machine learning are rapidly advancing and are increasingly applied to the field of mental health, including psychotherapy (Burr & Floridi, 2020; Torous et al., 2020). These technologies aim to assist individuals in learning and applying therapeutic skills, identifying behavioral patterns, and integrating interventions into daily life and by drawing on well-established approaches, such as cognitive behavioral therapy (CBT), positive psychology, and mindfulness (Prescott & Barnes, 2024). Some AI-based conversational agents and chatbots are even designed to simulate emotional intelligence with the goal of forming therapeutic alliances with users, clients, or patients (Darcy et al., 2021; Ghandeharioun et al., 2019).

Link to the article is here.

Here are some thoughts:

A central insight from the article is the critical distinction between "simulated" empathy and the genuine therapeutic alliance. The authors argue that while chatbots can be programmed to mimic emotional intelligence, they fundamentally lack the capacity for a true relational bond—a factor that research consistently identifies as the primary driver of healing in psychotherapy. This reinforces the view that a machine mirroring words back to a patient is functionally different from a human truly understanding them, and that removing the human from the loop risks stripping therapy of its most effective component.

Furthermore, the article raises significant alarms about the dangers of "datafication" and the potential for bias when human judgment is removed. The authors warn that reducing complex human experiences—such as trauma and personal history—into quantifiable data points for an algorithm can strip away the very humanity that therapy seeks to address. They explicitly caution against "blind reliance" on these tools, noting that AI models are often trained on limited or skewed datasets. Without a skilled clinician to interpret these suggestions with cultural humility and nuance, an automated system could actively harm vulnerable patients by misinterpreting their symptoms or reinforcing existing stereotypes.

Finally, the article touches on deep ethical questions that support my prior articles on this topic. It questions whether it is even ethically permissible for chatbots to feign empathy when they cannot actually feel it, suggesting that such deception undermines the core values of the profession. While the article admits that automation offers 24/7 accessibility, it concedes that AI lacks the adaptability and emotional support necessary for complex cases. Ultimately, the authors conclude that AI should be viewed strictly as an "adjunct"—a helper tool—rather than a replacement, confirming that the human professional remains the essential safeguard against the hollowness and risks of automated mental healthcare.

Exploring the Cognitive Sense of Self in AI: Ethical Frameworks and Technological Advances for Enhanced Decision-Making

Barnes, E., & Hutson, J. (2024).

International Journal of Recent Engineering Science,

11(6), 225–237.

https://doi.org/10.14445/23497157/ijres-v11i6p119

Abstract

The burgeoning field of Artificial Intelligence (AI) increasingly focuses on developing systems capable of self-awareness, merging technological innovation with deep ethical and philosophical considerations. This article explores the cognitive sense of self within AI, examining mechanisms through which AI systems may mirror human-like consciousness and self-perception. Despite significant advances, substantial gaps remain in the understanding and practical implementation of self-aware characteristics in AI, particularly in applying theoretical models and ethical frameworks to real-world scenarios. There is a pressing need for comprehensive research to explore these theoretical underpinnings and translate them into operationalsystems capable of ethical and adaptable behaviors. This study aims to synthesize existing knowledge, identify critical gaps in the literature, and highlight the implications of these findings for the future development of machine learning systems. Integrating insights from cognitive science, neuroscience, and ethical studies, this article seeks to provide a foundational framework for advancing emergent technologies that are both technologically robust and aligned with societal values. The significance of this research lies in its potential to guide the development of machine systems capable of complex decision-making and interactions, addressing both the moral and practical challenges of integrating such systems into daily human activities.

Here are some thoughts:

The ethical framework discussed in the paper rightfully highlights the risks of manipulation and the blurring of moral status. As an ethics expert, I am particularly concerned with the authors' note that these systems could modify their behaviors based on reinforcement learning to "optimize performance". In a healthcare or mental health context, if "performance" is defined as "user engagement," a self-aware AI might learn to manipulate human emotions to maximize interaction time, effectively weaponizing the user's empathy. Furthermore, the paper raises the issue of AI rights and whether self-aware systems deserve protection "akin to that provided to living beings". This creates a legal and moral quagmire in hospital settings: if a self-aware AI "refuses" a task based on its own derived "goals" or "motivational frameworks", does this constitute a malfunction or an exercise of autonomy? The authors’ call for "robust ethical guidelines" is critical, but we likely need entirely new categories of jurisprudence to handle "synthetic agency".

Why Artificial Intelligence Will Not Replace Human Psychologists: Legal, Ethical, and Clinical Limitations

Gavazzi, J. (2025, December).

Psychotherapy Bulletin, 61(1).

Clinical Impact Statement

The responsible integration of artificial intelligence (AI) into the practice of psychology requires that it functions strictly as a tool for human psychologists who must retain ultimate accountability for all clinical decisions. AI systems cannot replace empathy, judgment, and professional responsibility, which form the foundation of high-quality psychological care.

This article builds on previous arguments (Gavazzi, 2025a; Gavazzi, 2025b) stating that although AI technologies are rapidly advancing, they cannot replace human psychologists performing psychotherapy; this is simply the result of evolutionary advantages in humans across social, emotional, and cognitive domains that are essential for therapeutic interactions. In addition, these systems are unlikely to replace psychologists in the foreseeable future for practical reasons. Legal, ethical, and clinical barriers— particularly those involving state licensing, clinical judgments, forensic considerations, and accountability—make the deployment of autonomous systems in therapeutic settings impractical and potentially dangerous. This article presents key structural and philosophical reasons why human oversight and involvement remain essential in psychological practice.

The article is here.

Consciousness science: where are we, where are we going, and what if we get there?

Cleeremans, A., Mudrik, L., & Seth, A. K. (2025).

Frontiers in Science, 3.

https://doi.org/10.3389/fsci.2025.1546279

Abstract

Understanding the biophysical basis of consciousness remains a substantial challenge for 21st-century science. This endeavor is becoming even more pressing in light of accelerating progress in artificial intelligence and other technologies. In this article, we provide an overview of recent developments in the scientific study of consciousness and consider possible futures for the field. We highlight how several novel approaches may facilitate new breakthroughs, including increasing attention to theory development, adversarial collaborations, greater focus on the phenomenal character of conscious experiences, and the development and use of new methodologies and ecological experimental designs. Our emphasis is forward-looking: we explore what “success” in consciousness science may look like, with a focus on clinical, ethical, societal, and scientific implications. We conclude that progress in understanding consciousness will reshape how we see ourselves and our relationship to both artificial intelligence and the natural world, usher in new realms of intervention for modern medicine, and inform discussions around both nonhuman animal welfare and ethical concerns surrounding the beginning and end of human life.

Key Points:

• Understanding consciousness is one of the most substantial challenges of 21st-century science and is urgent due to advances in artificial intelligence (AI) and other technologies.
• Consciousness research is gradually transitioning from empirical identification of neural correlates of consciousness to encompass a variety of theories amenable to empirical testing.
• Future breakthroughs are likely to result from the following: increasing attention to the development of testable theories; adversarial and interdisciplinary collaborations; large-scale, multi-laboratory studies (alongside continued within-lab effort); new research methods (including computational neurophenomenology, novel ways to track the content of perception, and causal interventions); and naturalistic experimental designs (potentially using technologies such as extended reality or wearable brain imaging).
• Consciousness research may benefit from a stronger focus on the phenomenological, experiential aspects of conscious experiences.
• “Solving consciousness”—even partially—will have profound implications across science, medicine, animal welfare, law, and technology development, reshaping how we see ourselves and our relationships to both AI and the natural world.
• A key development would be a test for consciousness, allowing a determination or informed judgment about which systems/organisms—such as infants, patients, fetuses, animals, organoids, xenobots, and AI—are conscious.

Theory Is All You Need: AI, Human Cognition, and Causal Reasoning

Felin, T., & Holweg, M. (2024).

SSRN Electronic Journal.

https://doi.org/10.2139/ssrn.4737265

Abstract

Scholars argue that artificial intelligence (AI) can generate genuine novelty and new knowledge and, in turn, that AI and computational models of cognition will replace human decision making under uncertainty. We disagree. We argue that AI’s data-based prediction is different from human theory-based causal logic and reasoning. We highlight problems with the decades-old analogy between computers and minds as input–output devices, using large language models as an example. Human cognition is better conceptualized as a form of theory-based causal reasoning rather than AI’s emphasis on information processing and data-based prediction. AI uses a probability-based approach to knowledge and is largely backward looking and imitative, whereas human cognition is forward-looking and capable of generating genuine novelty. We introduce the idea of data–belief asymmetries to highlight the difference between AI and human cognition, using the example of heavier-than-air flight to illustrate our arguments. Theory-based causal reasoning provides a cognitive mechanism for humans to intervene in the world and to engage in directed experimentation to generate new data. Throughout the article, we discuss the implications of our argument for understanding the origins of novelty, new knowledge, and decision making under uncertainty.

Here are some thoughts:

This paper challenges the dominant view that artificial intelligence (AI), particularly large language models (LLMs), mirrors or will soon surpass human cognition. The authors argue against the widespread computational metaphor of the mind, which treats human thinking as data-driven, predictive information processing akin to AI. Instead, they emphasize that human cognition is fundamentally theory-driven and rooted in causal reasoning, experimentation, and the generation of novel, heterogenous beliefs—often in defiance of existing data or consensus. Drawing on historical examples like the Wright brothers, who succeeded despite prevailing scientific skepticism, the paper illustrates how human progress often stems from delusional-seeming ideas that later prove correct. Unlike AI systems that rely on statistical pattern recognition and next-word prediction from vast datasets, humans engage in counterfactual thinking, intentional intervention, and theory-building, enabling true innovation and scientific discovery. The authors caution against over-reliance on prediction-based AI in decision-making, especially under uncertainty, and advocate for a "theory-based view" of cognition that prioritizes causal understanding over mere correlation. In essence, they contend that while AI excels at extrapolating from the past, only human theory-making can generate genuinely new knowledge.

The narrow search effect and how broadening search promotes belief updating

Leung, E., & Urminsky, O. (2025).

PNAS, 122(13).

https://doi.org/10.1073/pnas.2408175122

Abstract

Information search platforms, from Google to AI-assisted search engines, have transformed information access but may fail to promote a shared factual foundation. We demonstrate that the combination of users’ prior beliefs influencing their search terms and the narrow scope of search algorithms can limit belief updating from search. We test this “narrow search effect” across 21 studies (14 preregistered) using various topics (e.g., health, financial, societal, political) and platforms (e.g., Google, ChatGPT, AI-powered Bing, our custom-designed search engine and AI chatbot interfaces). We then test user-based and algorithm-based interventions to counter the “narrow search effect” and promote belief updating. Studies 1 to 5 show that users’ prior beliefs influence the direction of the search terms, thereby generating narrow search results that limit belief updating. This effect persists across various domains (e.g., beliefs related to coronavirus, nuclear energy, gas prices, crime rates, bitcoin, caffeine, and general food or beverage health concerns; Studies 1a to 1b, 2a to 2g, 3, 4), platforms (e.g., Google—Studies 1a to 1b, 2a to 2g, 4, 5; ChatGPT, Study 3), and extends to consequential choices (Study 5). Studies 6 and 7 demonstrate the limited efficacy of prompting users to correct for the impact of narrow searches on their beliefs themselves. Using our custom-designed search engine and AI chatbot interfaces, Studies 8 and 9 show that modifying algorithms to provide broader results can encourage belief updating. These findings highlight the need for a behaviorally informed approach to the design of search algorithms.

Significance

In a time of societal polarization, the combination of people’s search habits and the search tools they use being optimized for relevance may perpetuate echo chambers. We document this across various diverse studies spanning health, finance, societal, and political topics on platforms like Google, ChatGPT, AI-powered Bing, and our custom-designed search engine and AI chatbot platforms. Users’ biased search behaviors and the narrow optimization of search algorithms can combine to reinforce existing beliefs. We find that algorithm-based interventions are more effective than user-based interventions to mitigate these effects. Our findings demonstrate the potential for behaviorally informed search algorithms to be a better tool for retrieving information, promoting the shared factual understanding necessary for social cohesion.

A pdf is here.

Here are some thoughts:

For psychologists, this work is a compelling demonstration of how classic cognitive biases operate in modern digital environments and how they can be mitigated not just by changing minds, but by changing the systems that shape information exposure. It calls for greater interdisciplinary collaboration between psychology, human-computer interaction, and AI ethics to design technologies that support, rather than hinder, rational belief updating and informed decision-making.

Clinically, psychologists can now better understand that resistance to change may not stem solely from emotional defenses or entrenched schemas, but also from how people actively seek information in narrow, belief-consistent ways. Crucially, the findings show that structural interventions—like guiding patients to consider broader perspectives or exposing them to balanced evidence—can be more effective than simply urging them to “reflect” on their thinking. This supports the use of active cognitive restructuring techniques in therapy, such as examining multiple viewpoints or generating alternative explanations, to counteract the natural tendency toward narrow search. 

Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI

Goktas, P., & Grzybowski, A. (2025).

Journal of clinical medicine, 14(5), 1605.

https://doi.org/10.3390/jcm14051605

Abstract

Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, and societal challenges. Key concerns include data privacy risks, algorithmic bias, and regulatory gaps that struggle to keep pace with AI advancements. This study aims to synthesize a multidisciplinary framework for trustworthy AI in healthcare, focusing on transparency, accountability, fairness, sustainability, and global collaboration. It moves beyond high-level ethical discussions to provide actionable strategies for implementing trustworthy AI in clinical contexts. 

Methods: A structured literature review was conducted using PubMed, Scopus, and Web of Science. Studies were selected based on relevance to AI ethics, governance, and policy in healthcare, prioritizing peer-reviewed articles, policy analyses, case studies, and ethical guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives from clinicians, ethicists, policymakers, and technologists, offering a holistic "ecosystem" view of AI. No clinical trials or patient-level interventions were conducted. 

Results: The analysis identifies key gaps in current AI governance and introduces the Regulatory Genome-an adaptive AI oversight framework aligned with global policy trends and Sustainable Development Goals. It introduces quantifiable trustworthiness metrics, a comparative analysis of AI categories for clinical applications, and bias mitigation strategies. Additionally, it presents interdisciplinary policy recommendations for aligning AI deployment with ethical, regulatory, and environmental sustainability goals. This study emphasizes measurable standards, multi-stakeholder engagement strategies, and global partnerships to ensure that future AI innovations meet ethical and practical healthcare needs. 

Conclusions: Trustworthy AI in healthcare requires more than technical advancements-it demands robust ethical safeguards, proactive regulation, and continuous collaboration. By adopting the recommended roadmap, stakeholders can foster responsible innovation, improve patient outcomes, and maintain public trust in AI-driven healthcare.

A pdf is here.

Here are some thoughts:

This article is important to psychologists as it addresses the growing role of artificial intelligence (AI) in healthcare and emphasizes the ethical, legal, and societal implications that psychologists must consider in their practice. It highlights the need for transparency, accountability, and fairness in AI-based health technologies, which can significantly influence patient behavior, decision-making, and perceptions of care. The article also touches on issues such as patient trust, data privacy, and the potential for AI to reinforce biases, all of which are critical psychological factors that impact treatment outcomes and patient well-being. Additionally, it underscores the importance of integrating human-centered design and ethics into AI development, offering psychologists insights into how they can contribute to shaping AI tools that align with human values, promote equitable healthcare, and support mental health in an increasingly digital world.

The Advantages of Human Evolution in Psychotherapy: Adaptation, Empathy, and Complexity

Gavazzi, J. (2025, May 24).

On Board with Professional Psychology.

American Board of Professional Psychology.

Issues 5.

Here is a link to the article.

Abstract

The rapid advancement of artificial intelligence, particularly Large Language Models (LLMs), has generated significant concern among psychologists regarding potential impacts on therapeutic practice. 

This paper examines the evolutionary advantages that position human psychologists as irreplaceable in psychotherapy, despite technological advances. Human evolution has produced sophisticated capacities for genuine empathy, social connection, and adaptive flexibility that are fundamental to effective therapeutic relationships. These evolutionarily-derived abilities include biologically-rooted emotional understanding, authentic empathetic responses, and the capacity for nuanced, context-dependent decision-making. In contrast, LLMs lack consciousness, genuine emotional experience, and the evolutionary framework necessary for deep therapeutic insight. While LLMs can simulate empathetic responses through linguistic patterns, they operate as statistical models without true emotional comprehension or theory of mind. The therapeutic alliance, cornerstone of successful psychotherapy, depends on authentic human connection and shared experiential understanding that transcends algorithmic processes. Human psychologists demonstrate adaptive complexity in understanding attachment styles, trauma responses, and individual patient needs that current AI cannot replicate.

The paper concludes that while LLMs serve valuable supportive roles in documentation, treatment planning, and professional reflection, they cannot replace the uniquely human relational and interpretive aspects essential to psychotherapy. Psychologists should integrate these technologies as resources while maintaining focus on the evolutionarily-grounded human capacities that define effective therapeutic practice.

A Framework for Language Technologies in Behavioral Research and Clinical Applications: Ethical Challenges, Implications, and Solutions

Diaz-Asper, C., Hauglid, M. K., et al. (2024).

American Psychologist, 79(1), 79–91.

https://doi.org/10.1037/amp0001195

Abstract

Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about human thought and communication, evaluate a variety of clinical conditions, and predict cognitive and psychological states. These innovations can be leveraged to automate traditionally time-intensive assessment tasks (e.g., educational assessment), provide psychological information and care (e.g., chatbots), and when delivered remotely (e.g., by mobile phone or wearable sensors) promise underserved communities greater access to health care. Indeed, the automatic analysis of speech provides a wealth of information that can be used for patient care in a wide range of settings (e.g., mHealth applications) and for diverse purposes (e.g., behavioral and clinical research, medical tools that are implemented into practice) and patient types (e.g., numerous psychological disorders and in psychiatry and neurology). However, automation of speech analysis is a complex task that requires the integration of several different technologies within a large distributed process with numerous stakeholders. Many organizations have raised awareness about the need for robust systems for ensuring transparency, oversight, and regulation of technologies utilizing artificial intelligence. Since there is limited knowledge about the ethical and legal implications of these applications in psychological science, we provide a balanced view of both the optimism that is widely published on and also the challenges and risks of use, including discrimination and exacerbation of structural inequalities.

Public Significance Statement

Computational advances in the domains of automatic speech recognition, natural language processing, and machine learning allow for the rapid and accurate assessment of a person’s speech for numerous purposes. The widespread adoption of these technologies permits psychologists an opportunity to learn more about psychological function, interact in new ways with research participants and patients, and aid in the diagnosis and management of various cognitive and mental health conditions. However, we argue that the current scope of the APA’s Ethical Principles of Psychologists and Code of Conduct is insufficient to address the ethical issues surrounding the application of artificial intelligence. Such a gap in guidance results in the onus falling directly on psychologists to educate themselves about the ethical and legal implications of these emerging technologies potentially exacerbating the risk of their use in both research and practice.

The Implications of Diverse Human Moral Foundations for Assessing the Ethicality of Artificial Intelligence

Telkamp, J.B., Anderson, M.H. 

J Bus Ethics 178, 961–976 (2022).

https://doi.org/10.1007/s10551-022-05057-6

Abstract

Organizations are making massive investments in artificial intelligence (AI), and recent demonstrations and achievements highlight the immense potential for AI to improve organizational and human welfare. Yet realizing the potential of AI necessitates a better understanding of the various ethical issues involved with deciding to use AI, training and maintaining it, and allowing it to make decisions that have moral consequences. People want organizations using AI and the AI systems themselves to behave ethically, but ethical behavior means different things to different people, and many ethical dilemmas require trade-offs such that no course of action is universally considered ethical. How should organizations using AI—and the AI itself—process ethical dilemmas where humans disagree on the morally right course of action? Though a variety of ethical AI frameworks have been suggested, these approaches do not adequately address how people make ethical evaluations of AI systems or how to incorporate the fundamental disagreements people have regarding what is and is not ethical behavior. Drawing on moral foundations theory, we theorize that a person will perceive an organization’s use of AI, its data procedures, and the resulting AI decisions as ethical to the extent that those decisions resonate with the person’s moral foundations. Since people hold diverse moral foundations, this highlights the crucial need to consider individual moral differences at multiple levels of AI. We discuss several unresolved issues and suggest potential approaches (such as moral reframing) for thinking about conflicts in moral judgments concerning AI.

The article is paywalled, link is above.

Here are some additional points:

• The article raises important questions about the ethicality of AI systems. It is clear that there is no single, monolithic standard of morality that can be applied to AI systems. Instead, we need to consider a plurality of moral foundations when evaluating the ethicality of AI systems.

• The article also highlights the challenges of assessing the ethicality of AI systems. It is difficult to measure the impact of AI systems on human well-being, and there is no single, objective way to determine whether an AI system is ethical. However, the article suggests that a pluralistic approach to ethical evaluation, which takes into account a variety of moral perspectives, is the best way to assess the ethicality of AI systems.

• The article concludes by calling for more research on the implications of diverse human moral foundations for the ethicality of AI. This is an important area of research, and I hope that more research is conducted in this area in the future.