Moral Judgment and Decision Making

Bartels, D.  et al.(n.d.).

In The Wiley Blackwell Handbook of

Judgment and Decision Making.

https://www.researchgate.net/publication/265086754

Abstract

This chapter focuses on moral flexibility, a term that the authors use that people are strongly motivated to adhere to and affirm their moral beliefs in their judgments and choices, they really want to get it right, they really want to do the right thing, but context strongly influences which moral beliefs are brought to bear in a given situation. It reviews contemporary research on moral judgment and decision making, and suggests ways that the major themes in the literature relate to the notion of moral flexibility. The chapter explains what makes moral judgment and decision making unique. It also reviews three major research themes and their explananda: morally prohibited value trade-offs in decision making; rules, reason, and emotion in trade-offs; and judgments of moral blame and punishment. The chapter also comments on methodological desiderata and presents understudied areas of inquiry.

Here are some thoughts:

This chapter explores the psychology of moral judgment and decision-making. The authors argue that people are motivated to adhere to moral beliefs, but context strongly influences which beliefs are applied in a given situation, resulting in moral flexibility.  The chapter reviews three major research themes: moral value tradeoffs, the role of rules, reason, and emotion in moral tradeoffs, and judgments of moral blame and punishment.  The authors discuss normative ethical theories, including consequentialism (utilitarianism), deontology, and virtue ethics.  They also examine the influence of protected values and sacred values on moral decision-making, highlighting the conflict between rule-based and consequentialist decision strategies.  Furthermore, the chapter investigates the interplay of emotion, reason, automaticity, and cognitive control in moral judgment, discussing dual-process models, moral grammar, and the reconciliation of rules and emotions.  The authors explore factors influencing moral blame and punishment, including the role of intentions, outcomes, and character evaluations.  The chapter concludes by emphasizing the complexity of moral decision-making and the importance of considering contextual influences. 

Applying ideation-to-action theories to predict suicidal behavior among adolescents

Okado, I., Floyd, F. J. et al. (2021).

Journal of Affective Disorders, 295, 1292–1300.

https://doi.org/10.1016/j.jad.2021.08.137

Abstract

Background

Although many risk factors for adolescent suicidal behavior have been identified, less is known about distinct risk factors associated with the progression from suicide ideation to attempts. Based on theories grounded in the ideation-to-action framework, we used structural equation modeling to examine risk and protective factors associated with the escalation from suicide ideation to attempts in adolescents.

Methods

In this cross-sectional study, data from the 2013 and 2015 Hawaii High School Youth Risk Behavior Surveys (N = 8,113) were analyzed. The sample was 54.0% female and racially/ethnically diverse. Risk factors included depression, victimization, self-harm, violent behavior, disinhibition, and hard substance use, and protective factors included adult support, sports participation, academic achievement and school safety.

Results

One in 6 adolescents (16.4%) reported suicide ideation, and nearly 1 in 10 (9.8%) adolescents had made a suicide attempt. Overall, disinhibition predicted the escalation to attempts among adolescents with suicide ideation, and higher academic performance was associated with lower suicide attempt risk. Depression and victimization were associated with suicide ideation.

Limitations

This study examined data from the Youth Risk Behavior Survey, and other known risk factors such as anxiety and family history of suicide were not available in these data.

Conclusions

Findings provide guidance for targets for clinical interventions focused on suicide prevention. Programs that incorporate behavioral disinhibition may have the greatest potential for reducing suicide attempt risk in adolescents with suicidal thoughts.

Highlights

• Depression and victimization are associated with suicide ideation in adolescents.

• Disinhibition potentiates suicide attempt risk in adolescents with suicide ideation.

• Higher academic performance protects against adolescent suicide attempt.

AI Is the Black Mirror

Philip Ball

Nautil.us

Originally published 11 Dec 24

Here is an excerpt:

To understand AI algorithms, Vallor argues we should not regard them as minds. “We’ve been trained over a century by science fiction and cultural visions of AI to expect that when it arrives, it’s going to be a machine mind,” she tells me. “But what we have is something quite different in nature, structure, and function.”

Rather, we should imagine AI as a mirror, which doesn’t duplicate the thing it reflects. “When you go into the bathroom to brush your teeth, you know there isn’t a second face looking back at you,” Vallor says. “That’s just a reflection of a face, and it has very different properties. It doesn’t have warmth; it doesn’t have depth.” Similarly, a reflection of a mind is not a mind. AI chatbots and image generators based on large language models are mere mirrors of human performance. “With ChatGPT, the output you see is a reflection of human intelligence, our creative preferences, our coding expertise, our voices—whatever we put in.”

Even experts, Vallor says, get fooled inside this hall of mirrors. Geoffrey Hinton, the computer scientist who shared this year’s Nobel Prize in physics for his pioneering work in developing the deep-learning techniques that made LLMs possible, at an AI conference in 2024 that “we understand language in much the same way as these large language models.”

The entire article is here.

Here are some thoughts:

Ball's article examines the societal and ethical challenges posed by artificial intelligence, likening its potential consequences to the dystopian narratives of Black Mirror. The article warns of AI's capacity to exacerbate inequality, enable mass surveillance, and undermine privacy, while also raising concerns about its role in manipulating behavior and perpetuating biases. It calls for robust ethical frameworks and regulation to ensure AI development aligns with human values, emphasizing the need for a proactive approach to mitigate risks. This thought-provoking piece serves as a timely reminder of the dual-edged nature of AI and the importance of addressing its societal implications as we advance technologically.

AI tools are spotting errors in research papers: inside a growing movement

Nature Publishing Group. (2025).

Nature.

https://doi.org/10.1038/d41586-025-00648-5

Late last year, media outlets worldwide warned that black plastic cooking utensils contained worrying levels of cancer-linked flame retardants. The risk was found to be overhyped — a mathematical error in the underlying research suggested a key chemical exceeded the safe limit when in fact it was ten times lower than the limit. Keen-eyed researchers quickly showed that an artificial intelligence (AI) model could have spotted the error in seconds.

The incident has spurred two projects that use AI to find mistakes in the scientific literature. The Black Spatula Project is an open-source AI tool that has so far analysed around 500 papers for errors. The group, which has around eight active developers and hundreds of volunteer advisers, hasn’t made the errors public yet; instead, it is approaching the affected authors directly, says Joaquin Gulloso, an independent AI researcher based in Cartagena, Colombia, who helps to coordinate the project. “Already, it’s catching many errors,” says Gulloso. “It’s a huge list. It’s just crazy.”

The other effort is called YesNoError and was inspired by the Black Spatula Project, says founder and AI entrepreneur Matt Schlicht. The initiative, funded by its own dedicated cryptocurrency, has set its sights even higher. “I thought, why don’t we go through, like, all of the papers?” says Schlicht. He says that their AI tool has analysed more than 37,000 papers in two months. Its website flags papers in which it has found flaws – many of which have yet to be verified by a human, although Schlicht says that YesNoError has a plan to eventually do so at scale.

Both projects want researchers to use their tools before submitting work to a journal, and journals to use them before they publish, the idea being to avoid mistakes, as well as fraud, making their way into the scientific literature.

The info is here.

Here are some thoughts:

The article discusses how AI tools are being used to identify errors in scientific research papers. It highlights a specific case involving a study that exaggerated the toxicity of black plastic utensils, which has spurred the development of projects leveraging large language models (LLMs) to scrutinize research papers for inaccuracies. These AI tools aim to improve the reliability and integrity of scientific literature by systematically detecting potential flaws or misrepresentations in published studies.

Those who (enjoy to) hurt: The influence of dark personality traits on animal- and human directed sadistic pleasure

Lobbestael, J., Wolf, F., Gollwitzer, M.,

& Baumeister, R. F. (2024).

Journal of Behavior Therapy and

Experimental Psychiatry, 85, 101963.

https://doi.org/10.1016/j.jbtep.2024.101963

Abstract

Background and objectives

Sadistic pleasure – gratuitous enjoyment from inflicting pain on others – has devastating interpersonal and societal consequences. The current knowledge on non-sexual, everyday sadism – a trait that resides within the general population – is scarce. The present study therefore focussed on personality correlates of sadistic pleasure. It investigated the relationship between the Dark Triad traits, and both dispositional and state-level sadistic pleasure.

Methods

N = 120 participants filled out questionnaires to assess their level of Dark Triad traits, psychopathy subfactors, and dispositional sadism. Then, participants engaged in an animal-directed task in which they were led to believe that they were killing bugs; and in a human-directed task where they could ostensibly noise blasts another participant. The two behavioral tasks were administered within-subjects, in randomized order. Sadistic pleasure was captured by increases in reported pleasure from pre-to post-task.

Results

All Dark Triad traits related to increased dispositional sadism, with psychopathy showing the strongest link. The coldheartedness psychopathy subscale showed a unique combination with both self-reported sadism and increased pleasure following bug grinding.

Limitations

Predominantly female and student sample, limiting generalizability of findings.

Conclusions

Out of all Dark Triad components, psychopathy showed the strongest link with gaining pleasure from hurting others. The results underscore the differential predictive value of psychopathy’s subcomponents for sadistic pleasure. Coldheartedness can be considered especially disturbing because of its unique relationship to deriving joy from irreversible harm-infliction (i.e. killing bugs). Our findings further establish psychopathy – and especially its coldheartedness component – as the most adverse Dark Triad trait.

Here are some thoughts:

The research suggests that psychopathy, particularly its coldheartedness component, is the strongest predictor of sadistic pleasure. This has implications for the assessment and treatment of individuals with sadistic tendencies. Psychologists may find it useful to specifically evaluate psychopathy and its subcomponents when assessing such patients, and therapeutic interventions may need to specifically target psychopathic traits, especially coldheartedness. The study also found that psychopathy, but not narcissism or Machiavellianism, was associated with sadistic pleasure, suggesting that individuals high in psychopathy may derive pleasure from acts of violence. This has implications for assessing the risk of violent behavior in clinical and forensic settings. Future research could explore how other personality traits outside the Dark Triad relate to sadistic pleasure, and examine the impact of contextual factors on the personality-sadism link.

How AI can distort clinical decision-making to prioritize profits over patients

Katie Palmer

STATnews.com

Originally posted 3 March 25

More than a decade ago, Ken Mandl was on a call with a pharmaceutical company and the leader of a social network for people with diabetes. The drug maker was hoping to use the platform to encourage its members to get a certain lab test.

The test could determine a patient's need for a helpful drug. But in that moment, said Mandl, director of the computational health informatics program at Boston Children's Hospital, "I could see this focus on a biomarker as a way to increase sales of the product." To describe the phenomenon, he coined the term "biomarkup": the way commercial interests can influence the creation, adoption, and interpretation of seemingly objective measures of medical status.

These days, Mandl has been thinking about how the next generation of quantified outputs in health could be gamed: artificial intelligence tools.

"It is easy to imagine a new generation of Al-based revenue cycle management model tools that achieve higher reimbursements by nudging clinicians toward more lucrative care pathways," Mandl wrote in a recent perspective in NEJM AI. "Al-based decision support interventions are vulnerable across their entire development life cycle and could be manipulated to favor specific products or services."

The info is paywalled here.

Here are some thoughts:

Dr. Ken Mandl raises a critical concern about the potential for "biomarkup" in the age of artificial intelligence within healthcare. This concept, initially describing how commercial interests can manipulate seemingly objective medical measures, now extends to AI tools. Mandl warns that AI-driven systems, designed for tasks like revenue cycle management or clinical decision support, could be subtly manipulated to prioritize financial gain over patient well-being. This manipulation might involve nudging clinicians towards more lucrative care pathways or tuning algorithms to generate more referrals, particularly in fee-for-service models. The issue is exacerbated in direct-to-consumer healthcare, where profit motives may be even stronger and regulatory oversight potentially weaker. The ease with which financial outcomes can be measured, compared to patient outcomes, further compounds the problem, creating a risk of AI implementation being driven primarily by return on investment. Mandl emphasizes the urgent need for transparency in AI decision frameworks, ethical development practices, and careful regulatory oversight to safeguard patient interests and ensure that AI serves its intended purpose of improving healthcare, not just increasing profits.

Risk of Attempted and Completed Suicide in Persons Diagnosed With Headache

Elser, H., Farkas, D. K., et al. (2025).

JAMA Neurology.

https://doi.org/10.1001/jamaneurol.2024.4974

Abstract

Importance  Although past research suggests an association between migraine and attempted suicide, there is limited research regarding risk of attempted and completed suicide across headache disorders.

Objective  To examine the risk of attempted and completed suicide associated with diagnosis of migraine, tension-type headache, posttraumatic headache, and trigeminal autonomic cephalalgia (TAC).

Design, Setting, and Participants  This was a population-based cohort study of Danish citizens from 1995 to 2020. The setting was in Denmark, with a population of 5.6 million people. Persons 15 years and older who were diagnosed with headache were matched by sex and birth year to persons without headache diagnosis with a ratio of 5:1. Data analysis was conducted from May 2023 to May 2024.

Conclusions and Relevance  Results of this cohort study revealing the robust and persistent association of headache diagnoses with attempted and completed suicide suggest that behavioral health evaluation and treatment may be important for these patients.

Here are some thoughts:

This study identified a significant association between headache diagnoses and elevated risks of both attempted and completed suicide. The analysis revealed a robust and persistent link, with individuals diagnosed with headaches facing a disproportionately higher likelihood of suicidal behavior compared to the general population. While the study did not specify headache subtypes, the findings underscore the need for heightened mental health screening and intervention in patients with headache disorders. Researchers emphasized integrating suicide risk assessments into routine clinical care for this vulnerable population.

Implications for Practice

The results align with broader calls to address mental health comorbidities in chronic pain conditions. Primary care providers, in particular, are urged to adopt proactive strategies, such as safety planning and risk screening, to mitigate suicide risk in patients with headaches. Psychologists also need to identify headaches as a risk for suicide.

WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist

Ren, C., Zhang, Y., He, D., & Qin, J. 

(2024, June 16).

arXiv.org. https://arxiv.org/abs/2406.15474

Abstract

Large language models (LLMs) are raging over the medical domain, and their momentum has carried over into the mental health domain, leading to the emergence of few mental health LLMs. Although such mental health LLMs could provide reasonable suggestions for psychological counseling, how to develop an authentic and effective doctor-patient relationship (DPR) through LLMs is still an important problem. To fill this gap, we dissect DPR into two key attributes, i.e., the psychologist's empathy and proactive guidance. We thus present WundtGPT, an empathetic and proactive mental health large language model that is acquired by fine-tuning it with instruction and real conversation between psychologists and patients. It is designed to assist psychologists in diagnosis and help patients who are reluctant to communicate face-to-face understand their psychological conditions. Its uniqueness lies in that it could not only pose purposeful questions to guide patients in detailing their symptoms but also offer warm emotional reassurance. In particular, WundtGPT incorporates Collection of Questions, Chain of Psychodiagnosis, and Empathy Constraints into a comprehensive prompt for eliciting LLMs' questions and diagnoses. Additionally, WundtGPT proposes a reward model to promote alignment with empathetic mental health professionals, which encompasses two key factors: cognitive empathy and emotional empathy. We offer a comprehensive evaluation of our proposed model. Based on these outcomes, we further conduct the manual evaluation based on proactivity, effectiveness, professionalism and coherence. We notice that WundtGPT can offer professional and effective consultation. The model is available at huggingface. 

The research is here.

Here are some thoughts:

WundtGPT is an innovative large language model (LLM) specifically designed for mental health tasks. The model addresses three critical limitations in existing mental health LLMs: lack of goal-oriented diagnosis, insufficient proactive questioning, and ambiguous conceptualization of empathy.

The researchers developed WundtGPT by fine-tuning it using instruction and real-world conversation datasets between psychologists and patients. Its unique capabilities include posing purposeful questions to guide patients in detailing their symptoms and offering warm emotional reassurance. The model incorporates a comprehensive prompt strategy that includes a Collection of Questions, Chain of Psychodiagnosis, and Empathy Constraints.

A key innovation is the model's reward system, which promotes alignment with empathetic mental health professionals by encompassing two critical factors: cognitive empathy and emotional empathy. For cognitive empathy, the model uses an emotional detection task, while emotional empathy is aligned through reinforcement learning from human feedback.

The researchers evaluated WundtGPT from two perspectives: its ability to provide proactive diagnosis and deliver warm psychological consultation. The evaluation involved emotional benchmarking and expert assessments of the model's proactivity, effectiveness, professionalism, and coherence. Experimental results demonstrated that WundtGPT exhibits superior performance compared to baseline LLMs in simulated medical consultation scenarios.

Notably, WundtGPT is claimed to be the first proactive LLM specifically designed for mental health tasks, capable of assisting psychologists in diagnosis and helping patients who are reluctant to communicate face-to-face understand their psychological conditions.

Large Language Models Pass the Turing Test

Jones, C. R., & Bergen, B. K. (2025, March 31).

arXiv.org.

https://arxiv.org/abs/2503.23674

Abstract

We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time -- not significantly more or less often than the humans they were being compared to -- while baseline models (ELIZA and GPT-4o) achieved win rates significantly below chance (23% and 21% respectively). The results constitute the first empirical evidence that any artificial system passes a standard three-party Turing test. The results have implications for debates about what kind of intelligence is exhibited by Large Language Models (LLMs), and the social and economic impacts these systems are likely to have.

Here are some thoughts:

The study highlights significant advancements in AI technology, particularly in the capabilities of large language models (LLMs), as demonstrated by their ability to pass the Turing test. GPT-4.5 and LLaMa-3.1-405B, when given specific persona prompts, achieved win rates of 73% and 56%, respectively, meaning they were judged to be human more often than actual human participants in some cases. This marks the first robust empirical evidence that an AI system can pass the standard three-party Turing test, a major milestone in AI development. The success of these models underscores their ability to convincingly mimic human conversation, blurring the line between human and machine interaction.

A key factor in their performance was the use of tailored prompts. Models instructed to adopt a humanlike persona—such as a young, introverted individual familiar with internet culture—significantly outperformed those without such guidance. This adaptability demonstrates the flexibility of modern LLMs and their capacity to refine behavior based on contextual instructions. In contrast, older systems like ELIZA and GPT-4o performed poorly, with win rates of just 23% and 21%, highlighting the rapid progress in AI conversational abilities. The study also challenges the "ELIZA effect," showing that contemporary LLMs succeed not through superficial imitation but by replicating nuanced human conversational patterns.

Human interrogators often relied on social and emotional cues—such as humor, personality, and linguistic style—rather than traditional measures of intelligence to distinguish humans from AI. Despite some effective strategies, like "jailbreak" prompts or probing for inconsistencies, most participants struggled to reliably identify AI, further emphasizing the sophistication of these models. The findings suggest that LLMs can now effectively substitute for humans in short conversations, raising both opportunities and concerns. On one hand, this capability could enhance customer service, education, and entertainment. On the other, it poses ethical risks, including the potential for AI to be used in deception, social engineering, or the spread of misinformation.

Looking ahead, the study calls for further research into longer interactions, expert interrogators, and cultural common ground to better understand the limits of AI’s humanlike abilities. It also reignites philosophical debates about whether passing the Turing test truly reflects intelligence or merely advanced imitation. As AI continues to evolve, these advancements underscore the need for careful consideration of their societal impact, ethical implications, and the future of human-AI interaction.