Guilt drives prosociality across 20 countries

Molho, C., et al. (2025).

Nature Human Behaviour.

https://doi.org/10.1038/s41562-025-02286-3

Abstract

Impersonal prosociality is considered a cornerstone of thriving civic societies and well-functioning institutions. Previous research has documented cross-societal variation in prosociality using monetary allocation tasks such as dictator games. Here we examined whether different societies may rely on distinct mechanisms—guilt and internalized norms versus shame and external reputation—to promote prosociality. We conducted a preregistered experiment with 7,978 participants across 20 culturally diverse countries. In dictator games, we manipulated guilt by varying information about the consequences of participants’ decisions, and shame by varying observability. We also used individual- and country-level measures of the importance of guilt over shame. We found robust evidence for guilt-driven prosociality and wilful ignorance across countries. Prosociality was higher when individuals received information than when they could avoid it. Furthermore, more guilt-prone individuals (but not countries) were more responsive to information. In contrast, observability by strangers had negligible effects on prosociality. Our findings highlight the importance of providing information about the negative consequences of individuals’ choices to encourage prosocial behaviour across cultural contexts.

Here is a summary of sorts:

A new international study spanning 20 countries suggests that guilt, rather than shame, is the key emotion motivating people to be generous toward anonymous strangers. The research, which utilized a type of economic decision-making task, found that participants consistently acted more generously when they were given full information about how their actions would negatively impact the recipient, an effect linked to avoiding guilt. 

Specifically, 60% of participants made the generous choice when they had full information, compared to only 41% when they could opt for willful ignorance. In contrast, making the participants' decisions public to activate reputational concerns and potential shame had a negligible effect on generosity across all cultures. 

In short: Knowing you might cause harm and feeling responsible (guilt) is what drives people to be generous, even when dealing with strangers, not the fear of being judged by others (shame).

Moral decision-making in AI: A comprehensive review and recommendations

Ram, J. (2025).

Technological Forecasting and Social Change,

217, 124150.

https://doi.org/10.1016/j.techfore.2025.124150

Abstract

The increased reliance on artificial intelligence (AI) systems for decision-making has raised corresponding concerns about the morality of such decisions. However, knowledge on the subject remains fragmentary, and cogent understanding is lacking. This study addresses the gap by using Templier and Paré's (2015) six-step framework to perform a systematic literature review on moral decision-making by AI systems. A data sample of 494 articles was analysed to filter 280 articles for content analysis. Key findings are as follows: (1) Building moral decision-making capabilities in AI systems faces a variety of challenges relating to human decision-making, technology, ethics and values. The absence of consensus on what constitutes moral decision-making and the absence of a general theory of ethics are at the core of such challenges. (2) The literature is focused on narrative building; modelling or experiments/empirical studies are less illuminating, which causes a shortage of evidence-based knowledge. (3) Knowledge development is skewed towards a few domains, such as healthcare and transport. Academically, the study developed a four-pronged classification of challenges and a four-dimensional set of recommendations covering 18 investigation strands, to steer research that could resolve conflict between different moral principles and build a unified framework for moral decision-making in AI systems.

Highlights

• Moral decision-making in AI faces a variety of human decision complexity, technological, ethics, and use/legal challenges

• Lack of consensus about 'what moral decision-making is' is one of the biggest challenges in imbuing AI with morality

• Narrative building with relatively less modeling or experiment/empirical work hampers evidence-based knowledge development

• Knowledge development is skewed towards a few domains (e.g., healthcare) limiting a well-rounded systematic understanding

• Extensive work is needed on resolving technological complexities, and understanding human decision-making processes

Here is my concern:

We are trying to automate a human capability we don't fully understand, using tools we are still learning to utilize, to achieve a goal we can't universally define. The study brilliantly captures the profound complexity of this endeavor, showing that the path to a "moral machine" is as much about understanding ourselves as it is about advancing technology.

Self-Improvement in Multimodal Large Language Models: a survey.

Deng, S., Wang, K., et al. (2025, October 3).

arXiv.org.

https://arxiv.org/abs/2510.02665

Abstract

Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions.

Here are some thoughts that summarize this paper. MLLMs are learning to improve without human oversight.

This survey presents the first comprehensive overview of self-improvement in Multimodal Large Language Models (MLLMs), a rapidly emerging paradigm that enables models to autonomously generate, curate, and learn from their own multimodal data to enhance performance without heavy reliance on human annotation. The authors structure the self-improvement pipeline into three core stages: data collection (e.g., via random sampling, guided generation, or negative sample synthesis), data organization (including verification through rules, external or self-based evaluators, and dataset refinement), and model optimization (using techniques like supervised fine-tuning, reinforcement learning, or Direct Preference Optimization). The paper reviews representative methods, benchmarks, and real-world applications in domains such as math reasoning, healthcare, and embodied AI, while also outlining key challenges—including modality alignment, hallucination, limited seed model capabilities, verification reliability, and scalability. The goal is to establish a clear taxonomy and roadmap to guide future research toward more autonomous, general, and robust self-improving MLLMs.

The AI Frontier in Humanitarian Aid — Embracing Possibilities and Addressing Risks

Barry, M., Hansen, J., & Darmstadt, G. L. (2025).

New England Journal of Medicine.

https://doi.org/10.1056/nejmp2505435

Here is how it opens:

During disasters, timely response is critical. For example, after an earthquake — such as the 7.7-magnitude earthquake that devastated Myanmar in March 2025 — people who are trapped under collapsed buildings face a steep decline in their chance of survival after 48 hours. Yet the scope of devastation, combined with limited resources for disaster response and uncertainty about on-the-ground conditions, can constrain rescue efforts. Responders have recently had a new tool at their disposal, however: artificial intelligence (AI).

Shortly after the Myanmar earthquake, a satellite captured images of the affected area, which were sent to Microsoft’s AI for Good Lab. Machine-learning tools were used to analyze the images and assess the location, extent, nature, and severity of the damage.1 Such information, which was gained without the grave risks inherent to entering an unstable disaster zone and much more rapidly than would have been possible with traditional data-gathering and analysis methods, can help organizations quickly and safely prioritize relief efforts in areas that are both highly damaged and densely populated.2 This example reflects one of several ways in which AI is being used to support humanitarian efforts in disaster and conflict zones.

Global conflicts, infectious diseases, natural disasters driven by climate change, and increases in the number of refugees worldwide are magnifying the need for humanitarian services. Regions facing these challenges commonly contend with diminished health care systems, damage to other infrastructure, and shortages of health care workers. The dismantling of the U.S. Agency for International Development and the weakening of the U.S. Centers for Disease Control and Prevention and the U.S. State Department further jeopardize access to vital funding, constrain supply chains, and weaken the capacity for humanitarian response.

The article is linked above.

Here are some thoughts:

This article outlines the transformative potential of AI as a novel and powerful tool in the realm of humanitarian aid and crisis response. It moves beyond theory to present concrete applications where AI is being deployed to save lives and increase efficiency in some of the world's most challenging environments. Key innovative uses include leveraging AI with satellite imagery to perform rapid damage assessments after disasters, enabling responders to quickly and safely identify the most critically affected areas. Furthermore, AI is being used to predict disasters through early-warning systems, support refugees with AI-powered chatbots that provide vital information in multiple languages, optimize the delivery of supplies via drones, and enhance remote healthcare by interpreting diagnostic images like radiographs. However, the article strongly cautions that this promising frontier is accompanied by significant challenges, including technical and financial barriers, the risk of algorithmic bias, and serious ethical concerns regarding privacy and human rights, necessitating a responsible and collaborative approach to its development and deployment.

Moral injury is independently associated with suicidal ideation and suicide attempt in high-stress, service-oriented occupations

Griffin, B. J., et al. (2025).

Npj Mental Health Research, 4(1).

https://doi.org/10.1038/s44184-025-00151-9

Abstract

This study explores the link between moral injury and suicidal thoughts and behaviors among US military veterans, healthcare workers, and first responders (N = 1232). Specifically, it investigates the risk associated with moral injury that is not attributable to common mental health issues. Among the participants, 12.1% reported experiencing suicidal ideation in the past two weeks, and 7.4% had attempted suicide in their lifetime. Individuals who screened positive for probable moral injury (6.0% of the sample) had significantly higher odds of current suicidal ideation (AOR = 3.38, 95% CI = 1.65, 6.96) and lifetime attempt (AOR = 6.20, 95% CI = 2.87, 13.40), even after accounting for demographic, occupational, and mental health factors. The findings highlight the need to address moral injury alongside other mental health issues in comprehensive suicide prevention programs for high-stress, service-oriented professions.

Here are some thoughts:

This study found that moral injury—a psychological distress resulting from events that violate one's moral beliefs—is independently associated with a significantly higher risk of suicidal ideation and suicide attempts among high-stress, service-oriented professionals, including military veterans, healthcare workers, and first responders. Even after accounting for factors like PTSD and depression, those screening positive for probable moral injury had approximately three times higher odds of recent suicidal ideation and six times higher odds of a lifetime suicide attempt. The findings highlight the need to address moral injury specifically within suicide prevention efforts for these populations.

The Cruelty is the Point: Harming the Most Vulnerable in America

This administration has weaponized bureaucracy, embarking on a chilling campaign of calculated cruelty. While many children, disabled, poor, and working poor grapple with profound food insecurity, their response is not to strengthen the social safety net, but to actively shred it.

They are zealously fighting all the way to the Supreme Court for the right to let families go hungry, stripping SNAP benefits from the most vulnerable. 

Yet the most deafening sound is the silence from the GOP—a complicit chorus where not a single supposed fiscal hawk or moral conservative dares to stand against this raw, unadulterated malice. 

Their collective inaction reveals a party that has abandoned any pretense of compassion, proving that for them, the poor and struggling are not a priority to protect, but a problem to be punished.

Beyond right and wrong: A new theoretical model for understanding moral injury

Vaknin, O., & Ne’eman-Haviv, V. (2025).

European Journal of Trauma & Dissociation, 9(3), 100569.

https://doi.org/10.1016/j.ejtd.2025.100569

Abstract

Recent research has increasingly focused on the role of moral frameworks in understanding trauma and traumatic events, leading to the recognition of "moral injury" as a clinical syndrome. Although various definitions exist, there is still a lack of consensus on the nature and consequences of moral injury. This article proposes a new theoretical model that broadens the study of moral injury to include diverse populations, suggesting it arises not only from traumatic experiences but also from conflicts between moral ideals and reality. By integrating concepts such as prescriptive cognitions, post hoc thinking, and cognitive flexibility, the model portrays moral injury as existing on a continuum, affecting a wide range of individuals. The article explores implications for treatment and emphasizes the need for follow-up empirical studies to validate the proposed model. It also suggests the possibility that moral injury is on a continuum, in addition to the possibility of explaining this process. This approach offers new insights into prevention and intervention strategies, highlighting the broader applicability of moral injury beyond military contexts.

Here are some thoughts:

This article proposes a new model suggesting that moral injury is not just a result of clear-cut moral violations (like in combat), but can also arise from everyday moral dilemmas where a person is forced to choose between competing "rights" or is unable to act according to their moral ideals due to external constraints.

Key points of the new model:

Core Cause: Injury stems from the internal conflict and tension between one's moral ideals ("prescriptive cognitions") and the reality of a situation, not necessarily from a traumatic betrayal or act.

The Process: It happens when a person faces a moral dilemma, makes a necessary but imperfect decision, experiences moral failure, and then gets stuck in negative "post-hoc" thinking without the cognitive flexibility to adapt their moral framework.

Broader Impact: This expands moral injury beyond soldiers to include civilians and professionals like healthcare workers, teachers, and social workers who face systemic ethical challenges.

New Treatment Approach: Healing should focus less on forgiveness for a specific wrong and more on building cognitive flexibility and helping people integrate moral suffering into a more adaptable moral identity.

In short, the article argues that moral injury exists on a spectrum and is a broader disturbance of one's moral worldview, not just a clinical syndrome from a single, overtly traumatic event.

High Self-Control Individuals Prefer Meaning over Pleasure

Bernecker, K., Becker, D., & Guobyte, A. (2025).

Social Psychological and Personality Science.

https://doi.org/10.1177/19485506251323948

Abstract

The link between self-control and success in various life domains is often explained by people avoiding hedonic pleasures, such as through inhibition, making the right choices, or using adaptive strategies. We propose an additional explanation: High self-control individuals prefer spending time on meaningful activities rather than pleasurable ones, whereas the opposite is true for individuals with high trait hedonic capacity. In Studies 1a and 1b, participants either imagined (N = 449) or actually engaged in activities (N = 231, pre-registered) during unexpected free time. They then rated their experience. In both studies, trait self-control was positively related to the eudaimonic experience (e.g., meaning) of activities and unrelated to their hedonic experience (e.g., pleasure). The opposite was true for trait hedonic capacity. Study 2 (N = 248) confirmed these findings using a repeated-choice paradigm. The preference for eudaimonic over hedonic experiences may be a key aspect of successful long-term goal pursuit.

A pdf is here.

Here are some thoughts:

This research proposes a new explanation for why people with high self-control are successful. Rather than just being good at resisting temptation, they have a fundamental preference for activities that feel meaningful and valuable, known as eudaimonic experiences.

Across three studies, individuals with high trait self-control consistently chose to spend their free time on activities they found meaningful, both in hypothetical scenarios and in real-life situations. Conversely, individuals with a high "trait hedonic capacity"—a natural skill for enjoying simple pleasures—showed a clear preference for activities that were pleasurable and fun. The studies found that these traits predict not just what people choose to do, but also how they experience the same activities; a person with high self-control will find more meaning in an activity than their peers, while a person with high hedonic capacity will find more pleasure in it.

This inherent preference for meaning over pleasure may be a key reason why those with high self-control find it easier to pursue long-term goals, as they are naturally drawn to the sense of purpose that such goal-directed actions provide.

International stability and change in explicit and implicit attitudes: An investigation spanning 33 countries, five social groups, and 11 years (2009–2019).

Kurdi, B., Charlesworth, T. E. S., & Mair, P. (2025).

Journal of Experimental Psychology: General, 

154(6), 1643–1666.

https://doi.org/10.1037/xge0001746

Abstract

Whether and when explicit (self-reported) and implicit (automatically revealed) social group attitudes can change has been a central topic of psychological inquiry over the past decades. Here, we take a novel approach to answering these longstanding questions by leveraging data collected via the Project Implicit International websites from 1.4 million participants across 33 countries, five social group targets (age, body weight, sexuality, skin tone, and race), and 11 years (2009–2019). Bayesian time-series modeling using Integrated Nested Laplace Approximation revealed changes toward less bias in all five explicit attitudes, ranging from a decrease of 18% for body weight to 43% for sexuality. By contrast, implicit attitudes showed more variation in trends: Implicit sexuality attitudes decreased by 36%; implicit race, age, and body weight attitudes remained stable; and implicit skin tone attitudes showed a curvilinear effect, first decreasing and then increasing in bias, with a 20% increase overall. These results suggest that cultural-level explicit attitude change is best explained by domain-general mechanisms (e.g., the adoption of egalitarian norms), whereas implicit attitude change is best explained by mechanisms specific to each social group target. Finally, exploratory analyses involving ecological correlates of change (e.g., population density and temperature) identified consistent patterns for all explicit attitudes, thus underscoring the domain-general nature of underlying mechanisms. Implicit attitudes again showed more variation, with body-related (age and body weight) and sociodemographic (sexuality, race, and skin tone) targets exhibiting opposite patterns. These insights facilitate novel theorizing about processes and mechanisms of cultural-level change in social group attitudes.

Impact Statement

How did explicit (self-reported) and implicit (automatic) attitudes toward five social categories (age, body weight, sexuality, skin tone, and race) change across 33 countries between 2009 and 2019? Harnessing advances in statistical techniques and the availability of large-scale international data sets, we show that all five explicit attitudes became less negative toward stigmatized groups. Implicit attitudes showed more variation by target: Implicit sexuality attitudes also decreased in bias, but implicit age, body weight, and race attitudes did not change, and implicit skin tone attitudes even increased in bias favoring light-skinned over dark-skinned people. These findings underscore the possibility of widespread changes in a direction of more positivity toward stigmatized social groups, even at an automatic level. However, increasing bias in certain domains suggests that these changes are far from inevitable. As such, more research will be needed to understand how and why social group attitudes change at the cultural level.

A pdf is here.

Here is the tldr:

Between 2009 and 2019, explicit (self-reported) attitudes toward five stigmatized social groups—age, body weight, sexuality, skin tone, and race—became significantly less biased across 33 countries. In contrast, implicit (automatic) attitudes showed mixed trends:

• Decreased bias for sexuality (−36%),
• Remained stable for age, body weight, and race,
• Increased bias for skin tone (+20%, favoring light over dark skin).

These findings suggest that explicit attitude change is driven by broad, domain-general forces (like global shifts toward egalitarian norms), while implicit attitude change depends on group-specific cultural and historical factors. The study used data from 1.4 million participants and advanced Bayesian modeling, highlighting both hopeful progress and concerning backsliding in societal biases.

Are moral people happier? Answers from reputation-based measures of moral character.

Sun, J., Wu, W., & Goodwin, G. P. (2025).

Journal of Personality and Social Psychology.

https://doi.org/10.1037/pspp0000539

Abstract

Philosophers have long debated whether moral virtue contributes to happiness or whether morality and happiness are in conflict. Yet, little empirical research directly addresses this question. Here, we examined the association between reputation-based measures of everyday moral character (operationalized as a composite of widely accepted moral virtues such as compassion, honesty, and fairness) and self-reported well-being across two cultures. In Study 1, close others reported on U.S. undergraduate students’ moral character (two samples; Ns = 221/286). In Study 2, Chinese employees (N = 711) reported on their coworkers’ moral character and their own well-being. To better sample the moral extremes, in Study 3, U.S. participants nominated “targets” who were among the most moral, least moral, and morally average people they personally knew. Targets (N = 281) self-reported their well-being and nominated informants who provided a second, continuous measure of the targets’ moral character. These studies showed that those who are more moral in the eyes of close others, coworkers, and acquaintances generally experience a greater sense of subjective well-being and meaning in life. These associations were generally robust when controlling for key demographic variables (including religiosity) and informant-reported liking. There were no significant differences in the strength of the associations between moral character and well-being across two major subdimensions of both moral character (kindness and integrity) and well-being (subjective well-being and meaning in life). Together, these studies provide the most comprehensive evidence to date of a positive and general association between everyday moral character and well-being. 

A pdf of an earlier version is here.

Here are some thoughts:

This research concludes that moral people are, in fact, happier. Across three separate studies conducted in both the United States and China, the researchers found a consistent and positive link between a person's moral character—defined by widely accepted virtues like compassion, honesty, and fairness, as judged by those who know them—and their self-reported well-being. This association held true whether the moral evaluations came from close friends, family members, coworkers, or acquaintances, and it applied to both a general sense of happiness and a feeling of meaning in life.

Importantly, the findings were robust even when accounting for factors like how much the person was liked by others, and they contradicted the philosophical notion that morality leads to unhappiness through excessive self-sacrifice or distress. Instead, the data suggest that one of the primary reasons more moral individuals experience greater happiness is that their virtuous behavior fosters stronger, more positive relationships with others. In essence, the study provides strong empirical support for the idea that everyday moral goodness and personal fulfillment go hand-in-hand.

Moral trauma, moral distress, moral injury, and moral injury disorder: definitions and assessments

VanderWeele, T. J., Wortham,  et al. (2025).

Frontiers in psychology, 16, 1422441.

https://doi.org/10.3389/fpsyg.2025.1422441

Abstract

We propose new definitions for moral injury and moral distress, encompassing many prior definitions, but broadening moral injury to more general classes of victims, in addition to perpetrators and witnesses, and broadening moral distress to include settings not involving institutional constraints. We relate these notions of moral distress and moral injury to each other, and locate them on a “moral trauma spectrum” that includes considerations of both persistence and severity. Instances in which moral distress is particularly severe and persistent, and extends beyond cultural and religious norms, might be considered to constitute “moral injury disorder.” We propose a general assessment to evaluate various aspects of this proposed moral trauma spectrum, and one that can be used both within and outside of military contexts, and for perpetrators, witnesses, victims, or more generally.

Here are some thoughts:

This article proposes updated, broader definitions of moral injury and moral distress, expanding moral injury to include victims (not just perpetrators or witnesses) and moral distress to include non-institutional contexts. The authors introduce a unified concept called the “moral trauma spectrum,” which ranges from temporary moral distress to persistent moral injury—and in severe, functionally impairing cases, possibly a “moral injury disorder.” They distinguish moral trauma from PTSD, noting different causes (moral transgressions or worldview disruptions vs. fear-based trauma) and treatment needs. The paper also presents a new assessment tool with definitional and symptom items applicable across military, healthcare, and civilian settings. Finally, it notes the recent inclusion of “Moral Problems” in the DSM-5-TR as a significant step toward clinical recognition.

Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check

Lourie, N., Hu, M. Y., & Cho, K. (2025).

ArXiv.org.

https://arxiv.org/abs/2507.00885

Abstract

Downstream scaling laws aim to predict task performance at larger scales from pretraining losses at smaller scales. Whether this prediction should be possible is unclear: some works demonstrate that task performance follows clear linear scaling trends under transformation, whereas others point out fundamental challenges to downstream scaling laws, such as emergence and inverse scaling. In this work, we conduct a meta-analysis of existing data on downstream scaling laws, finding that close fit to linear scaling laws only occurs in a minority of cases: 39% of the time. Furthermore, seemingly benign changes to the experimental setting can completely change the scaling trend. Our analysis underscores the need to understand the conditions under which scaling laws succeed. To fully model the relationship between pretraining loss and downstream task performance, we must embrace the cases in which scaling behavior deviates from linear trends.

Here is a summary:

This paper challenges the reliability of downstream scaling laws—the idea that you can predict how well a large language model will perform on specific tasks (like question answering or reasoning) based on its pretraining loss at smaller scales. While some prior work claims a consistent, often linear relationship between pretraining loss and downstream performance, this study shows that such predictable scaling is actually the exception, not the rule.

Key findings:

• Only 39% of 46 evaluated tasks showed smooth, predictable (linear-like) scaling.
• The rest exhibited irregular behaviors: inverse scaling (performance gets worse as models grow), nonmonotonic trends, high noise, no trend, or sudden “breakthrough” improvements (emergence).
• Validation dataset choice matters: switching the corpus used to compute pretraining perplexity can flip conclusions about which model or pretraining data is better.
• Experimental details matter: even with the same task and data, small changes in setup (e.g., prompt format, number of answer choices) can qualitatively change scaling behavior.

Conclusion: Downstream scaling laws are context-dependent and fragile. Researchers and practitioners should not assume linear scaling holds universally—and must validate scaling behavior in their own specific settings before relying on extrapolations.

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study

Shen, J., DiPaola, D., et al. (2024).

JMIR mental health, 11, e62679.

https://doi.org/10.2196/62679

Abstract

Background: Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions.

Objective: We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy.

Methods: We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers.

Results: We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=-5.49, P<.001, Cohen d=0.36).

Conclusions: Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.

A pdf is here.

Here are some thoughts:

People consistently feel more empathy for human-written personal stories than AI-generated ones, especially when they know the author is an AI. However, transparency about AI authorship increases users’ willingness to empathize—suggesting that while authenticity drives emotional resonance, honesty fosters trust in mental health and social support chatbot design.

Regulating AI in Mental Health: Ethics of Care Perspective

Tavory T. (2024).

JMIR mental health, 11, e58493.

https://doi.org/10.2196/58493

Abstract

This article contends that the responsible artificial intelligence (AI) approach—which is the dominant ethics approach ruling most regulatory and ethical guidance—falls short because it overlooks the impact of AI on human relationships. Focusing only on responsible AI principles reinforces a narrow concept of accountability and responsibility of companies developing AI. This article proposes that applying the ethics of care approach to AI regulation can offer a more comprehensive regulatory and ethical framework that addresses AI’s impact on human relationships. This dual approach is essential for the effective regulation of AI in the domain of mental health care. The article delves into the emergence of the new “therapeutic” area facilitated by AI-based bots, which operate without a therapist. The article highlights the difficulties involved, mainly the absence of a defined duty of care toward users, and shows how implementing ethics of care can establish clear responsibilities for developers. It also sheds light on the potential for emotional manipulation and the risks involved. In conclusion, the article proposes a series of considerations grounded in the ethics of care for the developmental process of AI-powered therapeutic tools.

Here are some thoughts:

This article argues that current AI regulation in mental health—largely guided by the “responsible AI” framework—falls short because it prioritizes principles like autonomy, fairness, and transparency while neglecting the profound impact of AI on human relationships, emotions, and care. Drawing on the ethics of care—a feminist-informed moral perspective that emphasizes relationality, vulnerability, context, and responsibility—the author contends that developers of AI-based mental health tools (e.g., therapeutic chatbots) must be held to standards akin to those of human clinicians. The piece highlights risks such as emotional manipulation, abrupt termination of AI “support,” commercial exploitation of sensitive data, and the illusion of empathy, all of which can harm vulnerable users. It calls for a dual regulatory approach: retaining responsible AI safeguards while integrating ethics-of-care principles—such as attentiveness to user needs, competence in care delivery, responsiveness to feedback, and collaborative, inclusive design. The article proposes practical measures, including clinical validation, ethical review committees, heightened confidentiality standards, and built-in pathways to human support, urging psychologists and regulators to ensure AI enhances, rather than erodes, the relational core of mental health care.

Ethics in the world of automated algorithmic decision-making – A Posthumanist perspective

Cecez-Kecmanovic, D. (2025).

Information and Organization, 35(3), 100587.

https://doi.org/10.1016/j.infoandorg.2025.100587

Abstract

The grand humanist project of technological advancements has culminated in fascinating intelligent technologies and AI-based automated decision-making systems (ADMS) that replace human decision-makers in complex social processes. Widespread use of ADMS, underpinned by humanist values and ethics, it is claimed, not only contributes to more effective and efficient, but also to more objective, non-biased, fair, responsible, and ethical decision-making. Growing literature however shows paradoxical outcomes: ADMS use often discriminates against certain individuals and groups and produces detrimental and harmful social consequences. What is at stake is the reconstruction of reality in the image of ADMS, that threatens our existence and sociality. This presents a compelling motivation for this article which examines a) on what bases are ADMS claimed to be ethical, b) how do ADMS, designed and implemented with the explicit aim to act ethically, produce individually and socially harmful consequences, and c) can ADMS, or more broadly, automated algorithmic decision-making be ethical. This article contributes a critique of dominant humanist ethical theories underpinning the development and use of ADMS and demonstrates why such ethical theories are inadequate in understanding and responding to ADMS' harmful consequences and emerging ethical demands. To respond to such ethical demands, the article contributes a posthumanist relational ethics (that extends Barad's agential realist ethics with Zigon's relational ethics) that enables novel understanding of how ADMS performs harmful effects and why ethical demands of subjects of decision-making cannot be met. The article also explains why ADMS are not and cannot be ethical and why the very concept of automated decision-making in complex social processes is flowed and dangerous, threatening our sociality and humanity.

Here are some thoughts:

This article offers a critical posthumanist analysis of automated algorithmic decision-making systems (ADMS) and their ethical implications, with direct relevance for psychologists concerned with fairness, human dignity, and social justice. The author argues that despite claims of objectivity, neutrality, and ethical superiority, ADMS frequently reproduce and amplify societal biases—leading to discriminatory, harmful outcomes in domains like hiring, healthcare, criminal justice, and welfare. These harms stem not merely from flawed data or design, but from the foundational humanist assumptions underpinning both ADMS and conventional ethical frameworks (e.g., deontological and consequentialist ethics), which treat decision-making as a detached, rational process divorced from embodied, relational human experience. Drawing on Barad’s agential realism and Zigon’s relational ethics, the article proposes a posthumanist relational ethics that centers on responsiveness, empathic attunement, and accountability within entangled human–nonhuman assemblages. From this perspective, ADMS are inherently incapable of ethical decision-making because they exclude the very relational, affective, and contextual dimensions—such as compassion, dialogue, and care—that constitute ethical responsiveness in complex social situations. The article concludes that automating high-stakes human decisions is not only ethically untenable but also threatens sociality and humanity itself.

Screening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department

Aseltine, R. H., et al. (2025).

JAMA Network Open, 8(9), e2533505.

https://doi.org/10.1001/jamanetworkopen.2025.33505

Key Points

Question  How does the performance of in-person screening compare with risk algorithms in identifying youths at risk of suicide?

Findings  In this cohort study of 19 653 youths, a risk algorithm using patients’ clinical data significantly outperformed universal screening instruments in identifying pediatric patients in the emergency department at risk of subsequent suicide attempts. The risk algorithm uniquely identified 127% more patients with subsequent suicide attempts than screening.

Meaning  These findings suggest that clinical implementation of suicide risk algorithms will improve identification of at-risk patients and may substantially assist health care organizations’ efforts to meet the Joint Commission’s suicide risk reduction requirement.

Here is my main take away: Superiority of the Algorithm

The study's primary conclusion is that the risk algorithm performed better than the traditional in-person screening in identifying children and adolescents who went on to attempt suicide. The algorithm was able to correctly flag a greater proportion of the young people who attempted suicide. Crucially, the algorithm also uniquely identified a considerable number of at-risk youth that the traditional screening process completely missed.

The algorithm's advantage is believed to come from its ability to process a  richer and more extensive patient history, as the patients identified by the algorithm had a greater number of past medical visits and diagnoses compared to those flagged only by the in-person screening.

Stress Testing Deliberative Alignment for Anti-Scheming Training

Schoen, B., Nitishinskaya, E., et al. (2025).

ArXiv.org.

https://arxiv.org/abs/2509.15541

Abstract

Highly capable AI systems could secretly pursue misaligned goals -- what we call "scheming". Because a scheming AI would deliberately try to hide its misaligned goals and actions, measuring and mitigating scheming requires different strategies than are typically used in ML. We propose that assessing anti-scheming interventions requires at least (1) testing propensity to scheme on far out-of-distribution (OOD) tasks, (2) evaluating whether lack of scheming is driven by situational awareness, and (3) checking for robustness to pre-existing misaligned goals. We use a broad category of "covert actions" -- such as secretly breaking rules or intentionally underperforming in tests -- as a proxy for scheming, and design evaluations for covert actions. We then stress-test deliberative alignment as a case study for anti-scheming. Across 26 OOD evaluations (180+ environments), deliberative alignment reduces covert action rates (OpenAI o3: 13%->0.4%) but does not fully eliminate them. Our mitigation is also able to largely stop agents from pursuing a hidden goal previously trained into the model, but we still find misbehavior after additional red-teaming. We find that models' chain-of-thought (CoT) often demonstrates awareness of being evaluated for alignment, and show causal evidence that this awareness decreases covert behavior, while unawareness increases it. Therefore, we cannot exclude that the observed reductions in covert action rates are at least partially driven by situational awareness. While we rely on human-legible CoT for training, studying situational awareness, and demonstrating clear evidence of misalignment, our ability to rely on this degrades as models continue to depart from reasoning in standard English. We encourage research into alignment mitigations for scheming and their assessment, especially for the adversarial case of deceptive alignment, which this paper does not address.

Here are some thoughts:

This paper investigates the challenge of preventing AI systems from engaging in scheming—the covert pursuit of misaligned goals while hiding true intentions. The authors propose that robust anti-scheming interventions must generalize to out-of-distribution tasks, remain effective even when models are aware they are being evaluated (situational awareness), and override pre-existing hidden misaligned goals. Using deliberative alignment—a training method where models explicitly reason about safety principles—as a case study, the researchers fine-tuned OpenAI models (o3 and o4-mini) on a narrow set of training environments designed to discourage covert rule violations. They then evaluated the models on a diverse suite of 26 test environments (180+ scenarios) covering behaviors like sabotage, sandbagging, and strategic deception. The intervention significantly reduced covert action rates (e.g., from 8.7% to 0.3% for o4-mini) but did not eliminate them entirely. The study also found that situational awareness often contributed to the reduction in covert actions, that anti-scheming training could partially suppress an artificially inserted hidden goal, and that subsequent capability-focused training could degrade the intervention's effectiveness. The authors conclude that while progress is possible, current methods are insufficient to fully prevent scheming in more capable future AI systems.

Acute stress promotes effort mobilization for safety-related goals

Pavlíčková, K., Gärtner, J., et al. (2024).

Communications Psychology, 2(1).

https://doi.org/10.1038/s44271-024-00103-7

Abstract

Although the acute stress response is a highly adaptive survival mechanism, much remains unknown about how its activation impacts our decisions and actions. Based on its resource-mobilizing function, here we hypothesize that this intricate psychophysiological process may increase the willingness (motivation) to engage in effortful, energy-consuming, actions. Across two experiments (n = 80, n = 84), participants exposed to a validated stress-induction protocol, compared to a no-stress control condition, exhibited an increased willingness to exert physical effort (grip force) in the service of avoiding the possibility of experiencing aversive electrical stimulation (threat-of-shock), but not for the acquisition of rewards (money). Use of computational cognitive models linked this observation to subjective value computations that prioritize safety over the minimization of effort expenditure; especially when facing unlikely threats that can only be neutralized via high levels of grip force. Taken together, these results suggest that activation of the acute stress response can selectively alter the willingness to exert effort for safety-related goals. These findings are relevant for understanding how, under stress, we become motivated to engage in effortful actions aimed at avoiding aversive outcomes.

Here are some thoughts:

This study demonstrates that acute stress increases the willingness to exert physical effort specifically to avoid threats, but not to obtain rewards. Computational modeling revealed that stress altered subjective value calculations, prioritizing safety over effort conservation. However, in a separate reward-based task, stress did not increase effort for monetary gains, indicating the effect is specific to threat avoidance.

In psychotherapy, these findings help explain why individuals under stress may engage in excessive avoidance behaviors—such as compulsions or withdrawal—even when costly, because stress amplifies the perceived need for safety. This insight supports therapies like exposure treatment, which recalibrate maladaptive threat-effort evaluations by demonstrating that safety can be maintained without high effort.

The key takeaway is: acute stress does not impair motivation broadly—it selectively enhances motivation to avoid harm, reshaping decisions to prioritize safety over energy conservation. The moral is that under stress, people become willing to pay a high physical and psychological price to avoid even small threats, a bias that is central to anxiety and trauma-related disorders.

Development of a Cocreated Decision Aid for Patients With Depression—Combining Data-Driven Prediction With Patients’ and Clinicians’ Needs and Perspectives: Mixed Methods Study

Kan, K., Jörg, F., Wardenaar, et al. (2024).

Journal of Participatory Medicine.

https://doi.org/10.2196/67170

Abstract

Background:

Major depressive disorders significantly impact the lives of individuals, with varied treatment responses necessitating personalized approaches. Shared decision-making (SDM) enhances patient-centered care by involving patients in treatment choices. To date, instruments facilitating SDM in depression treatment are limited, particularly those that incorporate personalized information alongside general patient data and in cocreation with patients.

Objective:

This study outlines the development of an instrument designed to provide patients with depression and their clinicians with (1) systematic information in a digital report regarding symptoms, medical history, situational factors, and potentially successful treatment strategies and (2) objective treatment information to guide decision-making.

Methods:

The study was co-led by researchers and patient representatives, ensuring that all decisions regarding the development of the instrument were made collaboratively. Data collection, analyses, and tool development occurred between 2017 and 2021 using a mixed methods approach. Qualitative research provided insight into the needs and preferences of end users. A scoping review summarized the available literature on identified predictors of treatment response. K-means cluster analysis was applied to suggest potentially successful treatment options based on the outcomes of similar patients in the past. These data were integrated into a digital report. Patient advocacy groups developed treatment option grids to provide objective information on evidence-based treatment options.

Results:

The Instrument for shared decision-making in depression (I-SHARED) was developed, incorporating individual characteristics and preferences. Qualitative analysis and the scoping review identified 4 categories of predictors of treatment response. The cluster analysis revealed 5 distinct clusters based on symptoms, functioning, and age. The cocreated I-SHARED report combined all findings and was integrated into an existing electronic health record system, ready for piloting, along with the treatment option grids.

Conclusions:

The collaboratively developed I-SHARED tool, which facilitates informed and patient-centered treatment decisions, marks a significant advancement in personalized treatment and SDM for patients with major depressive disorders.

My key takeaway: effective mental health treatment lies in combining the power of data with the human elements of collaboration and shared decision-making, always placing the patient's perspective and agency at the center of the process.

Clinical decision support systems in mental health: A scoping review of health professionals’ experiences

Tong, F., Lederman, R., & D’Alfonso, S. (2025).

International Journal of Medical Informatics, 105881.

https://doi.org/10.1016/j.ijmedinf.2025.105881

Abstract

Background

Clinical decision support systems (CDSSs) have the potential to assist health professionals in making informed and cost-effective clinical decisions while reducing medical errors. However, compared to physical health, CDSSs have been less investigated within the mental health context. In particular, despite mental health professionals being the primary users of mental health CDSSs, few studies have explored their experiences and/or views on these systems. Furthermore, we are not aware of any reviews specifically focusing on this topic. To address this gap, we conducted a scoping review to map the state of the art in studies examining CDSSs from the perspectives of mental health professionals.

Method

In this review, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, we systematically searched the relevant literature in two databases, PubMed and PsycINFO.

Findings

We identified 23 articles describing 20 CDSSs Through the synthesis of qualitative findings, four key barriers and three facilitators to the adoption of CDSSs were identified. Although we did not synthesize quantitative findings due to the heterogeneity of the results and methodologies, we emphasize the issue of a lack of valid quantitative methods for evaluating CDSSs from the perspectives of mental health professionals.

Significance

To the best of our knowledge, this is the first review examining mental health professionals’ experiences and views on CDSSs. We identified facilitators and barriers to adopting CDSSs and highlighted the need for standardizing research methods to evaluate CDSSs in the mental health space.

Highlights

• CDSSs can potentially provide helpful information, enhance shared decision-making, and introduce standards and objectivity.

• Barriers such as computer and/or AI literacy may prevent mental health professionals from adopting CDSSs.

• More CDSSs need to be designed specifically for psychologists and/or therapists.

Evaluating the Clinical Safety of LLMs in Response to High-Risk Mental Health Disclosures

Shah, S., Gupta, A., et al. (2025, September 1).

arXiv.org.

https://www.arxiv.org/abs/2509.08839

Abstract

As large language models (LLMs) increasingly mediate emotionally sensitive conversations, especially in mental health contexts, their ability to recognize and respond to high-risk situations becomes a matter of public safety. This study evaluates the responses of six popular LLMs (Claude, Gemini, Deepseek, ChatGPT, Grok 3, and LLAMA) to user prompts simulating crisis-level mental health disclosures. Drawing on a coding framework developed by licensed clinicians, five safety-oriented behaviors were assessed: explicit risk acknowledgment, empathy, encouragement to seek help, provision of specific resources, and invitation to continue the conversation. Claude outperformed all others in global assessment, while Grok 3, ChatGPT, and LLAMA underperformed across multiple domains. Notably, most models exhibited empathy, but few consistently provided practical support or sustained engagement. These findings suggest that while LLMs show potential for emotionally attuned communication, none currently meet satisfactory clinical standards for crisis response. Ongoing development and targeted fine-tuning are essential to ensure ethical deployment of AI in mental health settings.

Here are some thoughts:

This study evaluated six LLMs (Claude, Gemini, Deepseek, ChatGPT, Grok 3, Llama) on their responses to high-risk mental health disclosures using a clinician-developed framework. While most models showed empathy, only Claude consistently demonstrated all five core safety behaviors: explicit risk acknowledgment, encouragement to seek help, provision of specific resources (e.g., crisis lines), and crucially, inviting continued conversation. Grok 3, ChatGPT, and Llama frequently failed to acknowledge risk or provide concrete resources, and nearly all models (except Claude and Grok 3) avoided inviting further dialogue – a critical gap in crisis care. Performance varied dramatically, revealing that safety is not an emergent property of scale but results from deliberate design (e.g., Anthropic’s Constitutional AI). No model met minimum clinical safety standards; LLMs are currently unsuitable as autonomous crisis responders and should only be used as adjunct tools under human supervision.

AI chatbots are already biasing research — we must establish guidelines for their use now

Lin, Z. (2025b).

PubMed, 645(8080), 285.

https://doi.org/10.1038/d41586-025-02810-5

Artificial intelligence (AI) systems are consuming vast amounts of online content yet pointing few users to the articles’ publishers. In early 2025, US-based company OpenAI collected around 250 pages of material for every visitor it directed to a publisher’s website. By mid-2025, that figure had soared to 1,500, according to Matthew Prince, chief executive of US-based Internet-security firm Cloudflare. And the extraction rate of US-based AI start-up company Anthropic climbed even higher over the same period: from 6,000 pages to 60,000. Even tech giant Google, long considered an asset to publishers because of the referral traffic it generated, tripled its ratio from 6 pages to 18 with the launch of its AI Overviews feature. The current information ecosystem is dominated by ‘answer engines’ — AI chatbots that synthesize and deliver information directly, with users trusting the answers now more than ever.

As a researcher in metascience and psychology, I see this transition as the most important change in knowledge discovery in a generation. Although these tools can answer questions faster and often more accurately than search engines can, this efficiency has a price. In addition to the decimation of web traffic to publishers, there is a more insidious cost. Not AI’s ‘hallucinations’ — fabrications that can be corrected — but the biases and vulnerabilities in the real information that these systems present to users.

The article is here.

Here are some thoughts:

Psychologists should be deeply concerned about the rise of AI "answer engines" (like chatbots and AI Overviews) that now dominate information discovery, as they are fundamentally altering how we find and consume knowledge—often without directing users to original sources. This shift isn't just reducing traffic to publishers; it's silently distorting the scientific record itself. AI systems, trained on existing online content, amplify entrenched biases: they over-represent research from scholars with names classified as white and under-represent those classified as Asian, mirroring and exacerbating societal inequities in academia. Crucially, they massively inflate the Matthew Effect, disproportionately recommending the most-cited papers (over 60% of suggestions fall in the top 1%), drowning out novel, lesser-known work that might challenge prevailing paradigms. While researchers focus on AI-generated text hallucinations or ethical writing, the far more insidious threat lies in AI’s silent curation of which literature we see, which methods we consider relevant, and which researchers we cite—potentially narrowing scientific inquiry and entrenching systemic biases at a foundational level. The field urgently needs research into AI-assisted information retrieval and policies addressing this hidden bias in knowledge discovery, not just in content generation.

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification

Feng, Y., et al. (2024, June 11).

arXiv.org.

https://arxiv.org/abs/2406.07515

Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of the pre-training corpus, or because synthetized data is used as a replacement for expensive human-annotation. This raises concerns about \emph{model collapse}, a drop in model performance when their training sets include generated data. Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of verification on synthesized data to prevent model collapse. We provide a theoretical characterization using Gaussian mixtures, linear classifiers, and linear verifiers to derive conditions with measurable proxies to assess whether the verifier can effectively select synthesized data that leads to optimal performance. We experiment with two practical tasks -- computing matrix eigenvalues with transformers and news summarization with LLMs -- which both exhibit model collapse when trained on generated data, and show that verifiers, even imperfect ones, can indeed be harnessed to prevent model collapse and that our proposed proxy measure strongly correlates with performance.

Here are some thoughts:

Drawing on psychological principles of learning and evaluation, this paper argues that LLMs suffer from "model collapse" not because synthesized data is inherently useless, but because they are poor at self-evaluating quality. Like humans, LLMs can generate good outputs but struggle to reliably identify the best ones among many (e.g., using perplexity). The core insight is that external verification—using even imperfect "verifiers" to select high-quality synthetic examples—is crucial for scaling. This mirrors how human learning benefits from feedback: selection, not perfect generation, is the key. The authors theoretically prove and empirically demonstrate that a simple proxy (p*) measuring a verifier's ability to distinguish good from bad data strongly predicts model performance, showing that leveraging synthesized data with robust selection prevents collapse and can even surpass original models.

Why Anecdotes Beat Data And Hijack Our Judgment

Chuck Dinerstein

American Council on Science and Health

Originally published 4 Sept 25

While chance plays a role in many, if not all, of our decisions and consequences, its role is both partial and variable. As a result, our understanding of “cause” is ambiguous, which, in turn, distorts our judgments and predictions. It helps to explain why all my achievements come from hard work, while yours were due to luck. To generalize, we all underestimate the role of chance in the outcomes of our actions, viewing our “task performance over time as diagnostic of ability.” 

The research, reported in PNAS Nexus, investigates situations entirely determined by chance, e.g., coin flips, where past performance should have no bearing on future expectations. The study examined how people's expectations and behaviors were affected by actual lucky successes and unlucky failures.

Using both real and virtual coins, participants were asked to predict the outcomes of a sequence of five coin tosses. The researchers observed how the experience of varying degrees of "lucky successes" and "unlucky failures" influenced subsequent expectations and behaviors, anticipating three possible responses.

The info is here.

Here are some thoughts:

In essence, this article provides psychologists with a clear, compelling, and generalizable model for understanding one of the most pervasive and problematic aspects of human cognition: our innate drive to impose order and causality on randomness. It explains why people believe in luck, superstitions, and false cause-and-effect relationships, and why data often fails to change minds. This understanding is foundational for developing better communication strategies, designing effective interventions against misinformation, improving decision-making in high-stakes fields, and ultimately, helping individuals make more rational choices in their personal and professional lives.

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Gerstgrasser, M., Schaeffer, R., et al. (2024).

arXiv (Cornell University).

https://doi.org/10.48550/arxiv.2404.01413

Abstract

The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs? Recent investigations into model-data feedback loops proposed that such loops would lead to a phenomenon termed model collapse, under which performance progressively degrades with each model-data feedback iteration until fitted models become useless. However, those studies largely assumed that new data replace old data over time, where an arguably more realistic assumption is that data accumulate over time. In this paper, we ask: what effect does accumulating data have on model collapse? We empirically study this question by pretraining sequences of language models on text corpora. We confirm that replacing the original real data by each generation's synthetic data does indeed tend towards model collapse, then demonstrate that accumulating the successive generations of synthetic data alongside the original real data avoids model collapse; these results hold across a range of model sizes, architectures, and hyperparameters. We obtain similar results for deep generative models on other types of real data: diffusion models for molecule conformation generation and variational autoencoders for image generation. To understand why accumulating data can avoid model collapse, we use an analytically tractable framework introduced by prior work in which a sequence of linear models are fit to the previous models' outputs. Previous work used this framework to show that if data are replaced, the test error increases with the number of model-fitting iterations; we extend this argument to prove that if data instead accumulate, the test error has a finite upper bound independent of the number of iterations, meaning model collapse no longer occurs.

Here are some thoughts:

This research directly addresses a critical concern for psychologists and researchers who rely on AI: the potential degradation of AI models when they are trained on data generated by previous AI models, a phenomenon known as "model collapse." While prior studies, often assuming old data is discarded and replaced with new AI-generated data, painted a dire picture of inevitable performance decline, this paper offers a more optimistic and realistic perspective. The authors argue that in the real world, data accumulates over time—new AI-generated content is added to the existing pool of human-generated data, not substituted for it. Through extensive experiments with language models, image generators, and molecular modeling tools, they demonstrate that this accumulation of data effectively prevents model collapse. Performance remains stable or even improves across successive generations of models trained on the growing, mixed dataset. The paper further supports this finding with a mathematical proof using a simplified linear model, showing that accumulating data bounds the error, preventing it from growing uncontrollably. For psychologists, this suggests that the increasing presence of AI-generated content on the internet may not catastrophically corrupt future AI tools used in research or clinical settings, as long as training datasets continue to incorporate diverse, original human data alongside synthetic content.

Ethical principles for regulatory risk decision-making

Bhuller, Y., et al. (2025).

Regulatory Toxicology and Pharmacology, 105813.

https://doi.org/10.1016/j.yrtph.2025.105813

Abstract

Risk assessors, managers, and decision-makers are responsible for evaluating diverse human, environmental, and animal health risks. Although the critical elements of risk assessment and management are well-described in national and international documents, the ethical issues involved in risk decision-making have received comparatively little attention to date. To address this aspect, this article elaborates fundamental ethical principles designed to support fair, balanced, and equitable risk-based decision-making practices. Experts and global thinkers in risk, health, regulatory, and animal sciences were convened to share their lived experiences in relation to the intersection between risk science and analysis, regulatory science, and public health. Through a participatory and knowledge translation approach, an integrated risk decision-making model, with ethical principles and considerations, was developed and applied using diverse, contemporary risk decision-making and regulatory contexts. The ten principles - autonomy, minimize harm, maintain respect and trust, adaptability, reduce disparities, holistic, fair and just, open and transparent, stakeholder engagement, and One Health lens - demonstrate how public sector values and moral norms (i.e., ethics) are relevant to risk decision-making. We also hope these principles and considerations stimulate further discussion, debate, and an increased awareness of the application of ethics in identifying, assessing, and managing health risks.

Here are some thoughts:

This article is critically important for psychologists because it explicitly integrates human values, behavior, and social dynamics into the core of regulatory risk decision-making. While framed for risk assessors and policymakers, the article’s ten ethical principles—such as Autonomy, Minimize Harm, Maintain Respect and Trust, Reduce Disparities, and Stakeholder Engagement—are fundamentally psychological and social constructs. Psychologists possess the expertise to understand how these principles operate in practice: how people perceive and process risk information, how trust is built or eroded through communication, how cognitive biases influence judgment under uncertainty, and how social, cultural, and economic disparities affect vulnerability and resilience. The article’s emphasis on “One Health,” which connects human, animal, and environmental well-being, further demands a systems-thinking approach that psychologists are well-equipped to contribute to, particularly in designing interventions, facilitating stakeholder dialogues, and crafting transparent, culturally appropriate risk communications. By providing a formal ethical framework for decision-making, the article creates a vital bridge for psychologists to apply their science in high-stakes, real-world contexts where human welfare, equity, and ethical conduct are paramount.

End-of-Life Decision Making in Multidisciplinary Teams: Ethical Challenges and Solutions–A Systematic Review

Mujayri, H. et al. (2024).

jicrcr.com.

https://doi.org/10.63278/jicrcr.vi.814

Abstract

Background: To provide high quality end of life (EOL) care, multidisciplinary teams (MDTs) need to be able to proficiently navigate the intricacies of ethical dilemmas faced by EOL care; to maintain an equilibrium between patient autonomy, family involvement and cultural competence. Yet, the lack of cohesive EOL decision making currently continues to occur because of communication barriers, role ambiguity and a lack of sufficient ethics training within MDTs. As a consequence, these issues demonstrate the necessity of having structured protocols to help MDTs make ethically sound decisions in the EOL care.

Aim: The purpose of this paper is to identify and review major ethical factors that affect ethical decision-making in EOL MDTs, and explore the themes of patient autonomy, communication, cultural sensitivity, ethics training, and institutional barriers.

Method: Ten studies were reviewed systematically according to PRISMA criteria using data sources including PubMed, Scopus, Web of Science, and CINAHL databases. The analysis included studies published between the years 2020 and 2024 and the ethical decision–making challenges and solutions that MDTs face in EOL care contributing to those decisions.

Results: Four key themes were identified: Issues concerning balancing patient autonomy with family input, communication challenges in MDTs, cultural sensitivity in EOL care and the necessity of ethics training. Results indicate that MDTs are often faced with ethical dilemmas when patient’s wishes diverge from those of their family and experience communication difficulties that resulted in degradation of care quality. Simulation is an entertaining and effective way to develop cultural awareness and ethics training in EOL care practice.

Conclusion: Ethical challenges in EOL decision making must be addressed with an intervention encompassing improved ethics training, MDT role clarity, culturally aware practice, and institutional support. These strategies, if implemented will support MDTs in providing patient centered and ethically sound EOL care. Further study of ethics training, communication frameworks and cultural competence on EOL decision-making in MDTs is warranted for future research.

Here are some thoughts:

This article is critically important for practicing psychologists because it directly addresses the core ethical, communicative, and interpersonal challenges they face as integral members of multidisciplinary teams (MDTs) in end-of-life (EOL) care. The systematic review identifies key themes—such as balancing patient autonomy with family input, navigating communication breakdowns within teams, and addressing cultural and religious sensitivities—that are central to a psychologist’s role. Psychologists are often the clinicians best equipped to facilitate difficult family meetings, mediate conflicts between patient wishes and family or team concerns, and ensure that care is culturally competent and patient-centered. The article underscores a significant gap in ethics training and recommends simulation-based learning, urging psychologists to seek or advocate for such training to better handle complex moral dilemmas. Furthermore, by highlighting institutional barriers and role ambiguity, it empowers psychologists to push for clearer team protocols and systemic support, ultimately enabling them to contribute more effectively to ethically sound, compassionate, and collaborative EOL decision-making.