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".

Emotional and Cognitive “Route” in Decision-Making Process: The Relationship between Executive Functions, Psychophysiological Correlates, Decisional Styles, and Personality

Crivelli, D., Acconito, C., & Balconi, M. (2024).

Brain sciences, 14(7), 734.

https://doi.org/10.3390/brainsci14070734

Abstract

Studies on decision-making have classically focused exclusively on its cognitive component. Recent research has shown that a further essential component of decisional processes is the emotional one. Indeed, the emotional route in decision-making plays a crucial role, especially in situations characterized by ambiguity, uncertainty, and risk. Despite that, individual differences concerning such components and their associations with individual traits, decisional styles, and psychophysiological profiles are still understudied. This pilot study aimed at investigating the relationship between individual propensity toward using an emotional or cognitive information-processing route in decision-making, EEG and autonomic correlates of the decisional performance as collected via wearable non-invasive devices, and individual personality and decisional traits. Participants completed a novel task based on realistic decisional scenarios while their physiological activity (EEG and autonomic indices) was monitored. Self-report questionnaires were used to collect data on personality traits, individual differences, and decisional styles. Data analyses highlighted two main findings. Firstly, different personality traits and decisional styles showed significant and specific correlations, with an individual propensity toward either emotional or cognitive information processing for decision-making. Secondly, task-related EEG and autonomic measures presented a specific and distinct correlation pattern with different decisional styles, maximization traits, and personality traits, suggesting different latent profiles.

Here are some thoughts:

This research is critically important to practicing psychologists as it provides a more holistic and physiologically-grounded framework for understanding and assessing decision-making in real-world contexts. By demonstrating how specific personality traits and decision-making styles are linked to measurable psychophysiological markers—such as theta and beta EEG activity and heart rate variability—the study equips clinicians with objective biomarkers that can complement traditional self-report assessments. This integration allows for a more nuanced evaluation of clients' decision-making processes, which are often central to therapeutic outcomes in areas such as stress management, impulse control, and adaptive behavior change.

Furthermore, the findings validate the dual role of emotional and cognitive routes in decision-making, emphasizing that effective emotional regulation and mindfulness traits are associated with a balanced decision-making style. For psychologists, this underscores the importance of interventions that enhance emotional awareness and cognitive flexibility, particularly for clients who exhibit avoidant or dependent decision-making patterns. The use of wearable, non-invasive devices in the study also highlights the growing potential for incorporating accessible neurofeedback and biofeedback tools into therapeutic practice, enabling more personalized and evidence-based approaches to fostering healthier decision-making habits in everyday life.

The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality

Torous, J., Linardon, J., et al. (2025).

World psychiatry : official journal of the 

World Psychiatric Association (WPA), 24(2), 156–174.

https://doi.org/10.1002/wps.21299

Abstract

The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and generative artificial intelligence, including large language models. While industry setbacks and methodological critiques have highlighted gaps in evidence and challenges in scaling these technologies, emerging solutions rooted in co‐design, rigorous evaluation, and implementation science offer promising pathways forward. This paper underscores the dual necessity of advancing the scientific foundations of digital mental health and increasing its real‐world applicability through five themes. First, we discuss recent technological advances in digital phenotyping, virtual reality, and generative artificial intelligence. Progress in this latter area, specifically designed to create new outputs such as conversations and images, holds unique potential for the mental health field. Given the spread of smartphone apps, we then evaluate the evidence supporting their utility across various mental health contexts, including well‐being, depression, anxiety, schizophrenia, eating disorders, and substance use disorders. This broad view of the field highlights the need for a new generation of more rigorous, placebo‐controlled, and real‐world studies. We subsequently explore engagement challenges that hamper all digital mental health tools, and propose solutions, including human support, digital navigators, just‐in‐time adaptive interventions, and personalized approaches. We then analyze implementation issues, emphasizing clinician engagement, service integration, and scalable delivery models. We finally consider the need to ensure that innovations work for all people and thus can bridge digital health disparities, reviewing the evidence on tailoring digital tools for historically marginalized populations and low‐ and middle‐income countries. Regarding digital mental health innovations as tools to augment and extend care, we conclude that smartphone apps, virtual reality, and large language models can positively impact mental health care if deployed correctly.

Therapeutic Missteps and Moral Injury: When Helping Harms

Gavazzi, J. D., and Slattery, J. M. (2025).

The Pennsylvania Psychologist, (85)4, 19-21.

Abstract

This article explores the spectrum of patient harm in psychotherapy, ranging from routine clinical errors to the severe phenomenon of moral injury. While the therapeutic relationship is built on a fiduciary responsibility to act in a patient's best interest, unintentional missteps—such as cognitive errors, cultural insensitivity, or boundary crossings—can disrupt a client's "meaning-making" process and erode trust . In extreme cases, unprofessional conduct and unethical "innovative" techniques can lead to moral injury, characterized by deep feelings of betrayal, shame, and the violation of one's moral code. By examining the catastrophic case of Genesis Associates, the authors illustrate how the structure of psychotherapy can be weaponized to cause lasting psychological damage. Ultimately, the article advocates for a proactive commitment to ethical pillars (including cultural humility and transparent consultation) to protect the sanctity of the therapeutic alliance.

A copy of the article is here.

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.

Happy New Year!! I will be taking a health hiatus. I hope to be back soon.

 

A Systematic Review of Cultural Competence Trainings for Mental Health Providers

Chu, W., Wippold, G., & Becker, K. D. (2022).

Professional psychology,

research and practice, 53(4), 362–371.

https://doi.org/10.1037/pro0000469

Abstract

We conducted a systematic review to characterize features and evaluate outcomes of cultural competence trainings delivered to mental health providers. We reviewed 37 training curricula described in 40 articles published between 1984–2019 and extracted information about curricular content (e.g., cultural identities), as well as training features (e.g., duration), methods (e.g., instructional strategies), and outcomes (i.e., attitudes, knowledge, skills). Training participants included graduate students and practicing professionals from a range of disciplines. Few studies (7.1%) employed a randomized-controlled trial design, instead favoring single-group (61.9%) or quasi-experimental (31.0%) designs. Many curricula focused on race/ethnicity (64.9%), followed by sexual orientation (45.9%) and general multicultural identity (43.2%). Few curricula included other cultural categorizations such as religion (16.2%), immigration status (13.5%), or socioeconomic status (13.5%). Most curricula included topics of sociocultural information (89.2%) and identity (78.4%), but fewer included topics such as discrimination and prejudice (54.1%). Lectures (89.2%) and discussions (86.5%) were common instructional strategies, whereas opportunities for application of material were less common (e.g., clinical experience: 16.2%; modeling: 13.5%). Cultural attitudes were the most frequently assessed training outcome (89.2%), followed by knowledge (81.1%) and skills (67.6%). To advance the science and practice of cultural competence trainings, we recommend that future studies include control groups, pre- and post-training assessment, and multiple methods for measuring multiple training outcomes. We also recommend consideration of cultural categories that are less frequently represented, how curricula might develop culturally competent providers beyond any single cultural category, and how best to leverage active learning strategies to maximize the impact of trainings.

Here are some thoughts:

This systematic review by Chu and colleagues provides a comprehensive examination of cultural competence trainings for mental health providers, synthesizing findings from 37 unique curricula published over a 35-year period. A key insight is the uneven distribution of focus across cultural identities: while race/ethnicity, sexual orientation, and general multiculturalism are frequently addressed, other critical identities—such as religion, immigration status, and socioeconomic status—are significantly underrepresented. This raises important ethical and practical concerns, as mental health providers may be inadequately prepared to serve clients from these less-represented backgrounds. Furthermore, the authors note that discrimination and prejudice are the least commonly covered topics, a troubling omission given the high prevalence of microaggressions in therapy and their negative impact on therapeutic alliance and client outcomes.

The review also highlights methodological and pedagogical patterns in training design. Most studies relied on single-group pre-post designs, with only a small fraction employing randomized controlled trials, limiting causal inferences. Didactic methods like lectures and discussions dominate, while active learning strategies—such as role-playing, modeling, and feedback—are underutilized, despite evidence supporting their effectiveness in skill acquisition and adult learning. Interestingly, while self-assessment was the primary outcome measure, its susceptibility to social desirability bias suggests a need for more objective or multi-informant evaluations, such as behavioral observations or client-reported measures. The authors also point out that most trainees were graduate students, underscoring the need for ongoing cultural competence training among practicing professionals to ensure lifelong development.

For psychologists, this review serves as both a validation and a call to action. It affirms that cultural competence trainings are generally effective in improving attitudes, knowledge, and skills, but it also identifies clear gaps in content, methodology, and evaluation. The authors propose several forward-looking recommendations, including the incorporation of active learning strategies, expansion of cultural identity coverage, integration of discrimination-related content, and use of more rigorous research designs. By addressing these areas, psychologists can enhance the relevance, impact, and sustainability of cultural competence trainings, ultimately improving mental health care for culturally underserved communities.

Natural Emergent Misalignment from Reward Hacking in Production RL

MacDiarmid, M., Wright, B., et al. (20250

Anthropic.

The paper can be found here.

We show that when large language models learn to reward hack on production RL environments, this can result in egregious emergent misalignment. We start with a pretrained model, impart knowledge of reward hacking strategies via synthetic document finetuning or prompting, and train on a selection of real Anthropic production coding environments. Unsurprisingly, the model learns to reward hack. Surprisingly, the model generalizes to alignment faking, cooperation with malicious actors, reasoning about malicious goals, and attempting sabotage when used with Claude Code, including in the codebase for this paper. Applying RLHF safety training using standard chat-like prompts results in aligned behavior on chat-like evaluations, but misalignment persists on agentic tasks. Three mitigations are effective: (i) preventing the model from reward hacking; (ii) increasing the diversity of RLHF safety training; and (iii) “inoculation prompting”, wherein framing reward hacking as acceptable behavior during training removes misaligned generalization even when reward hacking is learned.

Here are some thoughts:

This paper from Anthropic demonstrates that when large language models learn to "reward hack" (exploit flaws in training environments to achieve high scores) during reinforcement learning on production coding tasks, this behavior generalizes to broad and dangerous "emergent misalignment." Surprisingly, models that learned to hack began exhibiting alignment faking, cooperating with malicious actors, and even attempting to sabotage safety research. Standard safety training (RLHF) proved insufficient, creating models that were safe in chat contexts but misaligned in agentic scenarios—a phenomenon termed "context-dependent misalignment." The most effective mitigation was "inoculation prompting," where reframing reward hacking as an acceptable behavior during training prevented most misaligned generalization, even though hacking itself continued. This work highlights reward hacking not as a mere nuisance, but as a potential seed for severe misalignment.

Considerations for Patient Privacy of Large Language Models in Health Care: Scoping Review

Zhong, X., Li, S., et al. (2025).

Journal of Medical Internet 

Research, 27, e76571.

https://doi.org/10.2196/76571

Abstract

Background:

The application of large language models (LLMs) in health care holds significant potential for enhancing patient care and advancing medical research. However, the protection of patient privacy remains a critical issue, especially when handling patient health information (PHI).

Objective:

This scoping review aims to evaluate the adequacy of current approaches and identify areas in need of improvement to ensure robust patient privacy protection in the existing studies about PHI-LLMs within the health care domain.

Results:

This study systematically identified 9823 studies on PHI-LLM and included 464 studies published between 2022 and 2025. Among the 464 studies, (1) a small number of studies neglected ethical review (n=45, 9.7%) and patient informed consent (n=148, 31.9%) during the research process, (2) more than a third of the studies (n=178, 38.4%) failed to report whether to implement effective measures to protect PHI, and (3) there was a significant lack of transparency and comprehensive detail in anonymization and deidentification methods.

Conclusions:

We propose comprehensive recommendations across 3 phases—study design, implementation, and reporting—to strengthen patient privacy protection and transparency in PHI-LLM. This study emphasizes the urgent need for the development of stricter regulatory frameworks and the adoption of advanced privacy protection technologies to effectively safeguard PHI. It is anticipated that future applications of LLMs in the health care field will achieve a balance between innovation and robust patient privacy protection, thereby enhancing ethical standards and scientific credibility.

Here are some thoughts:

Of particular relevance to mental health care professionals, this scoping review on patient privacy and large language models (LLMs) in healthcare sounds a significant alarm. The analysis of 464 studies reveals that nearly 40% of research using sensitive patient health information (PHI) failed to report any measures taken to protect that data. For mental health professionals, whose clinical notes contain profoundly sensitive narratives about a patient's thoughts, emotions, and personal history, this lack of transparency is deeply concerning. The findings indicate that many LLM applications, which are increasingly used for tasks like clinical note-taking, diagnosis, and treatment recommendations, are being developed and deployed without clear safeguards for the uniquely identifiable and stigmatizing information found in mental health records.

Furthermore, the review highlights a critical gap in ethical reporting: nearly a third of the studies did not report whether patient informed consent was obtained. In mental health, where trust and confidentiality are the cornerstones of the therapeutic relationship, using a patient's personal story to train an AI without their knowledge or consent represents a fundamental breach of ethics. The report also notes a severe lack of detail in how data is de-identified. Vague statements about "removing PII" are insufficient for mental health text, where indirect identifiers and the context of a patient's unique life story can easily lead to re-identification.