Why Do More Police Officers Die by Suicide Than in the Line of Duty?

Jaime Thompson

The New York Times

Originally published 8 May 25

Here is an excerpt:

American policing has paid much attention to the dangers faced in the line of duty, from shootouts to ambushes, but it has long neglected a greater threat to officers: themselves. More cops kill themselves every year than are killed by suspects. At least 184 public-safety officers die by suicide each year, according to First H.E.L.P., a nonprofit that has been collecting data on police suicide since 2016. An average of about 57 officers are killed by suspects every year, according to statistics from the Federal Bureau of Investigation. After analyzing data on death certificates, Dr. John Violanti, a research professor at the University at Buffalo, concluded that law-enforcement officers are 54 percent more likely to die by suicide than the average American worker. A lack of good data, however, has thwarted researchers, who have struggled to reach consensus on the problem’s scope. Recognizing the problem, Congress passed a law in 2020 requiring the F.B.I. to collect data on police suicide, but reporting remains voluntary.

“Suicide is something you just didn’t talk about in law enforcement,” says Chuck Wexler, the executive director of the Police Executive Research Forum (PERF). “It was shameful. It was weakness.” But a growing body of research has shown how chronic exposure to stress and trauma can impact the brain, causing impaired thinking, poor decision-making, a lack of empathy and difficulty distinguishing between real and perceived threats. Those were the very defects on display in the high-profile videos of police misconduct that looped across the country leading up to the killing of George Floyd by an officer in 2020. National outrage and widespread protests against the police were experienced as further stress by a force that already was, by many metrics, mentally and physically unwell. PERF now calls police suicide the “No. 1 officer-safety issue.”

The article is here.

Here are some thoughts:

Police officers unfortunately face a significantly elevated risk of suicide compared to the general population, a grim reality that tragically surpasses even the dangers they encounter in the line of duty. This heightened risk is often attributed to the cumulative impact of repeated exposure to traumatic events, which can lead to the development of mental health challenges such as post-traumatic stress disorder (PTSD), depression, and anxiety. Sadly, some officers may turn to substance abuse as a way to cope with these intense emotional burdens, which can further compound their difficulties. Research indicates that the rates of depression among law enforcement officers are nearly twice that of the general public, highlighting the profound psychological toll of their profession. Compounding this issue is the cultural environment within law enforcement, which can often discourage officers from seeking help for mental health concerns due to the prevailing stigma and fears of being perceived as weak or unfit for their duties. Consequently, there is a pressing need for the development and implementation of readily accessible and confidential mental health resources specifically designed to meet the unique needs of the law enforcement community. These resources should include peer support programs and trauma-informed care approaches to foster a culture of well-being and encourage officers to seek the support they deserve.

Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation

Tierney, A. A.,  et al. (2024).

NEJM Catalyst, 5(3).

https://doi.org/10.1056/cat.23.0404

Abstract

Clinical documentation in the electronic health record (EHR) has become increasingly burdensome for physicians and is a major driver of clinician burnout and dissatisfaction. Time dedicated to clerical activities and data entry during patient encounters also negatively affects the patient–physician relationship by hampering effective and empathetic communication and care. Ambient artificial intelligence (AI) scribes, which use machine learning applied to conversations to facilitate scribe-like capabilities in real time, has great potential to reduce documentation burden, enhance physician–patient encounters, and augment clinicians’ capabilities. The technology leverages a smartphone microphone to transcribe encounters as they occur but does not retain audio recordings. To address the urgent and growing burden of data entry, in October 2023, The Permanente Medical Group (TPMG) enabled ambient AI technology for 10,000 physicians and staff to augment their clinical capabilities across diverse settings and specialties. The implementation process leveraged TPMG’s extensive experience in large-scale technology instantiation and integration incorporating multiple training formats, at-the-elbow peer support, patient-facing materials, rapid-cycle upgrades with the technology vendor, and ongoing monitoring. In 10 weeks since implementation, the ambient AI tool has been used by 3,442 TPMG physicians to assist in as many as 303,266 patient encounters across a wide array of medical specialties and locations. In total, 968 physicians have enabled ambient AI scribes in ≥100 patient encounters, with one physician having enabled it to assist in 1,210 encounters. The response from physicians who have used the ambient AI scribe service has been favorable; they cite the technology’s capability to facilitate more personal, meaningful, and effective patient interactions and to reduce the burden of after-hours clerical work. In addition, early assessments of patient feedback have been positive, with some describing improved interaction with their physicians. Early evaluation metrics, based on an existing tool that evaluates the quality of human-generated scribe notes, find that ambient AI use produces high-quality clinical documentation for physicians’ editing. Further statistical analyses after AI scribe implementation also find that usage is linked with reduced time spent in documentation and in the EHR. Ongoing enhancements of the technology are needed and are focused on direct EHR integration, improved capabilities for incorporating medical interpretation, and enhanced workflow personalization options for individual users. Despite this technology’s early promise, careful and ongoing attention must be paid to ensure that the technology supports clinicians while also optimizing ambient AI scribe output for accuracy, relevance, and alignment in the physician–patient relationship.

Key Takeaways

• Ambient artificial intelligence (AI) scribes show early promise in reducing clinicians’ burden, with a regional pilot noting a reduction in the amount of time spent constructing notes among users.

• Ambient AI scribes were found to be acceptable among clinicians and patients, largely improving the experience of both parties, with some physicians noting the transformational nature of the technology on their care.

• Although a review of 35 AI-generated transcripts resulted in an average score of 48 of 50 in 10 key domains, AI scribes are not a replacement for clinicians. They can produce inconsistencies that require physicians’ review and editing to ensure that they remain aligned with the physician–patient relationship.

• Given the incredible pace of change, building a dynamic evaluation framework is essential to assess the performance of AI scribes across domains including engagement, effectiveness, quality, and safety.

This article won’t change your mind. Here’s why

Lubrano, S. S. (2025, May 18).

The Guardian.

Here is the link

Here is an excerpt:

There are lots of reasons why debate (and indeed, information-giving and argumentation in general) tends to be ineffective at changing people’s political beliefs. Cognitive dissonance, a phenomenon I studied as part of my PhD research, is one. This is the often unconscious psychological discomfort we feel when faced with contradictions in our own beliefs or actions, and it has been well documented. We can see cognitive dissonance and its effects at work when people rapidly “reason” in ways that are really attempts to mitigate their discomfort with new information about strongly held beliefs. For example, before Trump was convicted of various charges in 2024, only 17% of Republican voters believed felons should be able to be president; directly after his conviction, that number rose to 58%. To reconcile two contradictory beliefs (that presidents shouldn’t do x, and that Trump should be president), an enormous number of Republican voters simply changed their mind about the former. In fact, Republican voters shifted their views on more or less all the things Trump had been convicted of: fewer felt it was immoral to have sex with a porn star, pay someone to stay silent about an affair, or falsify a business record. Nor is this effect limited to Trump voters: research suggests we all rationalise in this way, in order to hold on to the beliefs that let us keep operating as we have been. Or, ironically, to change some of our beliefs in response to new information, but often only in order to not have to sacrifice other strongly held beliefs.

But it’s not just psychological phenomena like cognitive dissonance that make debates and arguments relatively ineffective. As I lay out in my book, probably the most important reason words don’t change minds is that two other factors carry far more influence: our social relationships; and our own actions and experiences.

Here are some thoughts:

The article discusses how people often resist changing their minds, even when presented with strong evidence, due to the psychological and social costs involved. It explains that beliefs are deeply tied to personal identity and social relationships, making individuals reluctant to alter them to avoid feelings of inconsistency or social rejection. The psychological mechanism at play is cognitive dissonance, where holding contradictory beliefs causes discomfort, leading people to reject new information that conflicts with their existing views. Additionally, motivated reasoning drives individuals to interpret evidence in a way that aligns with their preexisting beliefs to maintain emotional and social harmony. The article suggests that fostering open, non-confrontational discussions and emphasizing shared values can help reduce resistance to changing one’s mind, as it lessens the perceived threat to identity and social bonds.

Persuading people is a lot like psychotherapy because both require creating a safe, non-judgmental space where individuals can explore conflicting beliefs without feeling defensive, allowing change to emerge from within rather than through forceful confrontation.

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.

Artificial intelligence and free will: generative agents utilizing large language models have functional free will

Martela, F. (2025).

AI And Ethics.

https://doi.org/10.1007/s43681-025-00740-6

Abstract

Combining large language models (LLMs) with memory, planning, and execution units has made possible almost human-like agentic behavior, where the artificial intelligence creates goals for itself, breaks them into concrete plans, and refines the tactics based on sensory feedback. Do such generative LLM agents possess free will? Free will requires that an entity exhibits intentional agency, has genuine alternatives, and can control its actions. Building on Dennett’s intentional stance and List’s theory of free will, I will focus on functional free will, where we observe an entity to determine whether we need to postulate free will to understand and predict its behavior. Focusing on two running examples, the recently developed Voyager, an LLM-powered Minecraft agent, and the fictitious Spitenik, an assassin drone, I will argue that the best (and only viable) way of explaining both of their behavior involves postulating that they have goals, face alternatives, and that their intentions guide their behavior. While this does not entail that they have consciousness or that they possess physical free will, where their intentions alter physical causal chains, we must nevertheless conclude that they are agents whose behavior cannot be understood without postulating that they possess functional free will.

Here are some thoughts:

This article explores whether advanced AI systems, particularly generative agents using large language models (LLMs), possess free will. The author argues that while these AI agents may not have “physical free will,” meaning the ability to alter physical causal chains, they do exhibit “functional free will”. Functional free will is defined as the capacity to display intentional agency, recognize genuine alternatives, and control actions based on internal intentions. The article uses examples like Voyager, an AI agent in Minecraft, and Spitenik, a hypothetical autonomous drone, to illustrate how these systems meet the criteria for functional free will.

This research is important for psychologists because it challenges traditional views on free will, which often center on human consciousness and metaphysical considerations. It compels psychologists to reconsider how we attribute agency and decision-making to various entities, including AI, and how this attribution shapes our understanding of behavior

Large Language Model (LLM) Algorithms in Reshaping Decision-Making and Cognitive Biases in the AI-Leading World: An Experimental Study.

Khatoon, H., Khan, M. L., & Irshad, A. 

(2025, January 22). PsyArXiv

https://doi.org/10.31234/osf.io/xy82z

Abstract

The rise of artificial intelligence (AI) has accelerated decision-making since AI algorithmic recommendation may help reduce human limitations while increasing decision accuracy and efficiency. Large language model (LLM) algorithms are designed to enhance human decision-making competencies and remove possible cognitive biases. However, these algorithms can be biased and lead to poor decision-making. Building on previously existing LLM algorithm (i.e., ChatGPT and Perplexity.ai), this study examines whether users who get AI assistance during task-based decision-making have greater decision-making abilities than their peers who employ their own cognitive processes to make decisions. By using domain-independent LLM , incentives, and scenario-based task decisions, we find that the advice suggested by these AIs in the decisive situations were biased and wrong, and that resulted in poor decision outcomes. It has been observed that using public access LLM in crucial situations might result in both ineffective outcomes for the advisee and inadvertent consequences for third parties. Findings highlight the need of having an ethical AI algorithm and the ability to accurately assess trust in order to effectively deploy these systems. This raises concerns regarding the use of AI in decision making with careful assistance.

Here are some thoughts:

This research is important to psychologists because it examines how collaboration with large language models (LLMs) like ChatGPT affects human decision-making, particularly in relation to cognitive biases. By using a modified Adult Decision-Making Competence battery, the study offers empirical data on whether AI assistance improves or impairs judgment. It highlights the psychological dynamics of trust in AI, the risk of overreliance, and the ethical implications of using AI in decisions that impact others. These findings are especially relevant for psychologists interested in cognitive bias, human-technology interaction, and the integration of AI into clinical, organizational, and educational settings.

The Role of Emotion Dysregulation in Understanding Suicide Risk: A Systematic Review of the Literature

Rogante, E.,  et al. (2024).

Healthcare, 12(2), 169.

https://doi.org/10.3390/healthcare12020169

Abstract

Suicide prevention represents a global imperative, and efforts to identify potential risk factors are intensifying. Among these, emotional regulation abilities represent a transdiagnostic component that may have an impactful influence on suicidal ideation and behavior. Therefore, the present systematic review aimed to investigate the association between emotion dysregulation and suicidal ideation and/or behavior in adult participants. The review followed PRISMA guidelines, and the research was performed through four major electronic databases (PubMed/MEDLINE, Scopus, PsycInfo, and Web of Science) for relevant titles/abstracts published from January 2013 to September 2023. The review included original studies published in peer-reviewed journals and in English that assessed the relationship between emotional regulation, as measured by the Difficulties in Emotional Regulation Scale (DERS), and suicidal ideation and/or behavior. In total, 44 studies were considered eligible, and the results mostly revealed significant positive associations between emotion dysregulation and suicidal ideation, while the findings on suicide attempts were more inconsistent. Furthermore, the findings also confirmed the role of emotion dysregulation as a mediator between suicide and other variables. Given these results, it is important to continue investigating these constructs and conduct accurate assessments to implement effective person-centered interventions.

Here are some thoughts. I used this research in a recent article.

This systematic review explores the role of emotion dysregulation in understanding suicide risk among adults, analyzing 44 studies that assess the association between emotional regulation difficulties—measured primarily by the Difficulties in Emotion Regulation Scale (DERS)—and suicidal ideation and behavior. The findings largely support a significant positive correlation between emotion dysregulation and suicidal ideation across both clinical and nonclinical populations. Specific dimensions of emotion dysregulation, such as impulsivity, lack of emotional clarity, and ineffective use of regulatory strategies, were particularly linked to increased suicidal thoughts. However, results regarding suicide attempts were more inconsistent, with some studies showing a strong link while others found no significant associations.

The review also highlights the mediating role of emotion dysregulation between various risk factors (e.g., childhood trauma, psychopathy, depression) and suicidal outcomes. Emotion dysregulation appears to amplify suicide risk by influencing how individuals cope with psychological pain and stress. Despite methodological limitations—including reliance on self-report measures, sample heterogeneity, and limited longitudinal data—the evidence suggests that improving emotional regulation could be a valuable target for suicide prevention strategies. The authors recommend further research using robust statistical methods and comprehensive assessments to better understand causal pathways and enhance intervention effectiveness.

Ethical implication of artificial intelligence (AI) adoption in financial decision making.

Owolabi, O. S., Uche, P. C., et al. (2024).

Computer and Information Science, 17(1), 49.

https://doi.org/10.5539/cis.v17n1p49

Abstract

The integration of artificial intelligence (AI) into the financial sector has raised ethical concerns that need to be addressed. This paper analyzes the ethical implications of using AI in financial decision-making and emphasizes the importance of an ethical framework to ensure its fair and trustworthy deployment. The study explores various ethical considerations, including the need to address algorithmic bias, promote transparency and explainability in AI systems, and adhere to regulations that protect equity, accountability, and public trust. By synthesizing research and empirical evidence, the paper highlights the complex relationship between AI innovation and ethical integrity in finance. To tackle this issue, the paper proposes a comprehensive and actionable ethical framework that advocates for clear guidelines, governance structures, regular audits, and collaboration among stakeholders. This framework aims to maximize the potential of AI while minimizing negative impacts and unintended consequences. The study serves as a valuable resource for policymakers, industry professionals, researchers, and other stakeholders, facilitating informed discussions, evidence-based decision-making, and the development of best practices for responsible AI integration in the financial sector. The ultimate goal is to ensure fairness, transparency, and accountability while reaping the benefits of AI for both the financial sector and society.

Here are some thoughts:

This paper explores the ethical implications of using artificial intelligence (AI) in financial decision-making.  It emphasizes the necessity of an ethical framework to ensure AI is used fairly and responsibly.  The study examines ethical concerns like algorithmic bias, the need for transparency and explainability in AI systems, and the importance of regulations that protect equity, accountability, and public trust.  The paper also proposes a comprehensive ethical framework with guidelines, governance structures, regular audits, and stakeholder collaboration to maximize AI's potential while minimizing negative impacts.

These themes are similar to concerns in using AI in the practice of psychology. Also, psychologists may need to be aware of these issues for their own financial and wealth management.

The impact of AI errors in a human-in-the-loop process

Agudo, U., Liberal, K. G., et al. (2024).

Cognitive Research Principles and 

Implications, 9(1).

https://doi.org/10.1186/s41235-023-00529-3

Abstract

Automated decision-making is becoming increasingly common in the public sector. As a result, political institutions recommend the presence of humans in these decision-making processes as a safeguard against potentially erroneous or biased algorithmic decisions. However, the scientific literature on human-in-the-loop performance is not conclusive about the benefits and risks of such human presence, nor does it clarify which aspects of this human–computer interaction may influence the final decision. In two experiments, we simulate an automated decision-making process in which participants judge multiple defendants in relation to various crimes, and we manipulate the time in which participants receive support from a supposed automated system with Artificial Intelligence (before or after they make their judgments). Our results show that human judgment is affected when participants receive incorrect algorithmic support, particularly when they receive it before providing their own judgment, resulting in reduced accuracy. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/b6p4z/ Experiment 2 was preregistered.

Here are some thoughts:

This study explores the impact of AI errors in human-in-the-loop processes, where humans and AI systems collaborate in decision-making.  The research specifically investigates how the timing of AI support influences human judgment and decision accuracy.  The findings indicate that human judgment is negatively affected by incorrect algorithmic support, particularly when provided before the human's own judgment, leading to decreased accuracy.  This research highlights the complexities of human-computer interaction in automated decision-making contexts and emphasizes the need for a deeper understanding of how AI support systems can be effectively integrated to minimize errors and biases.    

This is important for psychologists because it sheds light on the cognitive biases and decision-making processes involved when humans interact with AI systems, which is an increasingly relevant area of study in the field.  Understanding these interactions can help psychologists develop interventions and strategies to mitigate negative impacts, such as automation bias, and improve the design of human-computer interfaces to optimize decision-making accuracy and reduce errors in various sectors, including public service, healthcare, and justice.