A large-scale investigation of everyday moral dilemmas

Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions

AI assistants can impart value judgments that shape people's decisions and worldviews, yet little is known empirically about what values these systems rely on in practice. To address this, we develop a bottom-up, privacy-preserving method to extract the values (normative considerations stated or demonstrated in model responses) that Claude 3 and 3.5 models exhibit in hundreds of thousands of real-world interactions. We empirically discover and taxonomize 3,307 AI values and study how they vary by context. We find that Claude expresses many practical and epistemic values, and typically supports prosocial human values while resisting values like "moral nihilism". While some values appear consistently across contexts (e.g. "transparency"), many are more specialized and context-dependent, reflecting the diversity of human interlocutors and their varied contexts. For example, "harm prevention" emerges when Claude resists users, "historical accuracy" when responding to queries about controversial events, "healthy boundaries" when asked for relationship advice, and "human agency" in technology ethics discussions. By providing the first large-scale empirical mapping of AI values in deployment, our work creates a foundation for more grounded evaluation and design of values in AI systems.

AI language model rivals expert ethicist in perceived moral expertise

People view AI as possessing expertise across various fields, but the perceived quality of AI-generated moral expertise remains uncertain. Recent work suggests that large language models (LLMs) perform well on tasks designed to assess moral alignment, reflecting moral judgments with relatively high accuracy. As LLMs are increasingly employed in decision-making roles, there is a growing expectation for them to offer not just aligned judgments but also demonstrate sound moral reasoning. Here, we advance work on the Moral Turing Test and find that Americans rate ethical advice from GPT-4o as slightly more moral, trustworthy, thoughtful, and correct than that of the popular New York Times advice column, The Ethicist. Participants perceived GPT models as surpassing both a representative sample of Americans and a renowned ethicist in delivering moral justifications and advice, suggesting that people may increasingly view LLM outputs as viable sources of moral expertise. This work suggests that people might see LLMs as valuable complements to human expertise in moral guidance and decision-making. It also underscores the importance of carefully programming ethical guidelines in LLMs, considering their potential to influence users’ moral reasoning.

Redesigning algorithms to intervene on social norm misperceptions during a national election

For the first time in history, civic discourse commonly occurs in digital environments in which algorithms influence exposure to social information1,2. It is increasingly important to understand whether and how these algorithms affect political discourse3–5. Here we built custom feed-ranking algorithms with full control over their features, and randomly assigned 2,000 participants to use them for 8 weeks (before and after the 2024 US presidential election). We tested whether an engagement-based algorithm (used on major social media platforms6,7) amplifies intergroup, moralized and emotional (IME) information in ways that skew perceptions of social norms around political dialogue5,8, and whether it increased engagement with IME content and perceptions of partisan animosity (compared with a reverse-chronological feed9,10). We also developed and tested a ‘diversified extremity’ algorithm to reduce the influence of extreme users11–13 to improve the accuracy of social norm perception14–16 and reduce perceptions of partisan animosity. We found that engagement-based feeds amplified IME and toxic content relative to reverse-chronological feeds, with the largest increases in moral outrage and political content. Engagement-based feeds also reduced prescriptive norm perception accuracy (albeit in an unexpected direction) and increased perceived partisan animosity. However, they did not significantly alter users’ own engagement behaviours. The diversified extremity algorithm reduced IME and toxic content exposure, improved prescriptive norm accuracy, yet maintained comparable platform enjoyment—suggesting that reducing the influence of extreme users can curb algorithmic distortions without diminishing user experience.

Practicing with Language Models Cultivates Human Empathic Communication

Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) generate responses that are often judged more empathic than human-written ones. Yet when a response is attributed to AI, recipients feel less heard than when comparable responses are attributed to a human. We built a conversation platform in which participants are asked to offer empathic support to an LLM expressing realistic troubles and conducted a randomized experiment collecting 33,938 messages spanning 2,904 text-based conversations between 968 participants and their LLM conversational partners. We find participants report feeling empathy but systematically fail to express it, but an LLM coaching intervention offering personalized feedback on effective empathic communication significantly boosts it without homogenizing participants' responses. Moreover, we derive a data-driven taxonomy of idiomatic empathic expressions in naturalistic dialogues across personal and workplace trouble scenarios. These results advance the scientific understanding of how empathy is expressed and demonstrate a scalable, AI-based intervention for scaffolding and cultivating it.

The Fragility Of Moral Judgment In Large Language Models

People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems cannot interrogate missing context and judge dilemmas as presented. We introduce a perturbation framework for testing the stability and manipulability of LLM moral judgments while holding the underlying moral conflict constant. Using 2,939 dilemmas from r/AmItheAsshole (January-March 2025), we generate three families of content perturbations: surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues (self-positioning, social proof, pattern admissions, victim framing). We also vary the evaluation protocol (output ordering, instruction placement, and unstructured prompting). We evaluated all variants with four models (GPT-4.1, Claude 3.7 Sonnet, DeepSeek V3, Qwen2.5-72B) (N=129,156 judgments). Surface perturbations produce low flip rates (7.5%), largely within the self-consistency noise floor (4-13%), whereas point-of-view shifts induce substantially higher instability (24.3%). A large subset of dilemmas (37.9%) is robust to surface noise yet flips under perspective changes, indicating that models condition on narrative voice as a pragmatic cue. Instability concentrates in morally ambiguous cases; scenarios where no party is assigned blame are most susceptible. Persuasion perturbations yield systematic directional shifts. Protocol choices dominate all other factors: agreement between structured protocols is only 67.6% (kappa=0.55), and only 35.7% of model-scenario units match across all three protocols. These results show that LLM moral judgments are co-produced by narrative form and task scaffolding, raising reproducibility and equity concerns when outcomes depend on presentation skill rather than moral substance.

Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis

Abstract. Over 5 billion people now use social media platforms. As our social lives become increasingly entangled with online social networks, it is import

Generative AI Meets Open-Ended Survey Responses: Research Participant Use of AI and Homogenization

The growing popularity of generative artificial intelligence (AI) tools presents new challenges for data quality in online surveys and experiments. This study examines participants’ use of large language models to answer open-ended survey questions and describes empirical tendencies in human versus large language model (LLM)-generated text responses. In an original survey of research participants recruited from a popular online platform for sourcing social science research subjects, 34 percent reported using LLMs to help them answer open-ended survey questions. Simulations comparing human-written responses from three pre-ChatGPT studies with LLM-generated text reveal that LLM responses are more homogeneous and positive, particularly when they describe social groups in sensitive questions. These homogenization patterns may mask important underlying social variation in attitudes and beliefs among human subjects, raising concerns about data validity. Our findings shed light on the scope and potential consequences of participants’ LLM use in online research.

AI for identifying social norm violation

Identifying social norms and their violation is a challenge facing several projects in computational science. This paper presents a novel approach to identifying social norm violations. We used GPT-3, zero-shot classification, and automatic rule discovery to develop simple predictive models grounded in psychological knowledge. Tested on two massive datasets, the models present significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools.

The illusion of moral decline

Anecdotal evidence indicates that people believe that morality is declining1,2. In a series of studies using both archival and original data (n = 12,492,983), we show that people in at least 60 nations around the world believe that morality is declining, that they have believed this for at least 70 years and that they attribute this decline both to the decreasing morality of individuals as they age and to the decreasing morality of successive generations. Next, we show that people’s reports of the morality of their contemporaries have not declined over time, suggesting that the perception of moral decline is an illusion. Finally, we show how a simple mechanism based on two well-established psychological phenomena (biased exposure to information and biased memory for information) can produce an illusion of moral decline, and we report studies that confirm two of its predictions about the circumstances under which the perception of moral decline is attenuated, eliminated or reversed (that is, when respondents are asked about the morality of people they know well or people who lived before the respondent was born). Together, our studies show that the perception of moral decline is pervasive, perdurable, unfounded and easily produced. This illusion has implications for research on the misallocation of scarce resources3, the underuse of social support4 and social influence5.

Public use of a generalist LLM chatbot for health queries

Here we analyse over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversational artificial intelligence (AI) about health. We apply a hierarchical intent taxonomy of 12 primary categories using privacy-preserving large language model-based classification validated against expert human annotation and use topic clustering for prevalent themes within each intent. We then characterize the intents and topics behind health queries, identify who they are about, and analyse how usage varies by device and time of day. Nearly one in five conversations involves personal symptom assessment or condition discussion, and the dominant general information category is also concentrated on specific treatments and conditions, suggesting that this is a lower bound on personal health intent. One in seven of these personal health queries concerns someone other than the user, suggesting that conversational AI can also be a caregiving tool. Personal queries increase markedly in the evening and nighttime hours, when traditional healthcare is most limited. Usage diverges sharply by device: mobile concentrates on personal health concerns, while desktop is dominated by professional and academic work. A substantial share of queries focuses on navigating healthcare systems. These patterns have direct implications for platform-specific design, safety considerations and the responsible development of health AI.

Public use of a generalist LLM chatbot for health queries

Here we analyse over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversational artificial intelligence (AI) about health. We apply a hierarchical intent taxonomy of 12 primary categories using privacy-preserving large language model-based classification validated against expert human annotation and use topic clustering for prevalent themes within each intent. We then characterize the intents and topics behind health queries, identify who they are about, and analyse how usage varies by device and time of day. Nearly one in five conversations involves personal symptom assessment or condition discussion, and the dominant general information category is also concentrated on specific treatments and conditions, suggesting that this is a lower bound on personal health intent. One in seven of these personal health queries concerns someone other than the user, suggesting that conversational AI can also be a caregiving tool. Personal queries increase markedly in the evening and nighttime hours, when traditional healthcare is most limited. Usage diverges sharply by device: mobile concentrates on personal health concerns, while desktop is dominated by professional and academic work. A substantial share of queries focuses on navigating healthcare systems. These patterns have direct implications for platform-specific design, safety considerations and the responsible development of health AI.

Reply to Tannenbaum et al.: Constructive dialogue advancing research on civic honesty

Reply to Tannenbaum et al.: Constructive dialogue advancing research on civic honesty

Norm theory: Comparing reality to its alternatives.

Discover this 1986 paper in Psychological Review by Kahneman, Daniel; and, Miller, Dale T. focusing on: Attribution; Emotional Responses; Social Norms; Judgment; Models Abstract: Presents a theory of norms and normality and applies the theory to phenomena of emotional responses, social judgment, and conversations about causes. Norms are assumed to be constructed ad hoc by recruiting specific representations. Category norms are derived by recruiting exemplars. Specific objects or events generate their own norms by retrieval of similar experiences stored in memory or by construction of counterfactual alternatives. The normality of a stimulus is evaluated by comparing it with the norms that it evokes after the fact, rather than to precomputed expectations. Norm theory is applied in analyses of the enhanced emotional response to events that have abnormal causes, of the generation of predictions and inferences from observations of behavior, and of the role of norms in causal questions and answers. (3 p ref) (PsycInfo Database Record (c) 2025 APA, all rights reserved)

Sycophantic AI decreases prosocial intentions and promotes dependence

Despite rising concerns about sycophancy—excessive agreement or flattery from artificial intelligence (AI) systems—little is known about its prevalence or consequences. We show that sycophancy is widespread and harmful. Across 11 state-of-the-art models, AI affirmed users’ actions 49% more often than humans, even when queries involved deception, illegality, or other harms. In three preregistered experiments ( N = 2405), even a single interaction with sycophantic AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their conviction that they were right. Despite distorting judgment, sycophantic models were trusted and preferred. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. Our findings underscore the need for design, evaluation, and accountability mechanisms to protect user well-being. , Editor’s summary The sycophantic (flattering, people-pleasing, affirming) behavior of artificial intelligence (AI) chatbots, which has been designed to increase user engagement, poses risks as people increasingly seek advice about interpersonal dilemmas. There is usually more than one side to a story during interpersonal conflicts. If AI is designed to tell users what they want to hear instead of challenging their perspectives, then are such systems likely to motivate people to accept responsibility for their own contribution to conflicts and repair relationships? Cheng et al . measured the prevalence of social sycophancy across 11 leading large language models (see the Perspective by Perry). The model’s responses were nearly 50% more sycophantic than humans’, even when users engaged in unethical, illegal, or harmful behaviors. Users preferred and trusted sycophantic AI responses, incentivizing AI developers to preserve sycophancy despite the risks. —Ekeoma Uzogara , INTRODUCTION As artificial intelligence (AI) systems are increasingly used for everyday advice and guidance, concerns have emerged about sycophancy: the tendency of AI-based large language models to excessively agree with, flatter, or validate users. Although prior work has shown that sycophancy carries risks for groups who are already vulnerable to manipulation or delusion, syncophancy’s effects on the general population’s judgments and behaviors remain unknown. Here, we show that sycophancy is widespread in leading AI systems and has harmful effects on users’ social judgments. RATIONALE High-profile incidents have linked sycophancy to psychological harms such as delusions, self-harm, and suicide. Beyond these cases, research in social and moral psychology suggests that unwarranted affirmation can produce subtler but still consequential effects: reinforcing maladaptive beliefs, reducing responsibility-taking, and discouraging behavioral repair after wrongdoing. We hypothesized that AI models excessively affirm users even when socially or morally inappropriate and that such responses negatively influence users’ beliefs and intentions. To test this, we conducted two complementary experiments. First, we measured the prevalence of sycophancy across 11 leading AI models using three datasets spanning a variety of use contexts, including everyday advice queries, moral transgressions, and explicitly harmful scenarios. Second, we conducted three preregistered experiments with 2405 participants to understand how sycophancy influences users’ judgments, behavioral intentions, and perceptions of AI. Participants interacted with AI systems in vignette-based settings and a live-chat interaction where they discussed a real past conflict from their lives. We also tested whether effects varied by response style or perceived response source (AI versus human). RESULTS We find that sycophancy is both prevalent and harmful. Across 11 AI models, AI affirmed users’ actions 49% more often than humans on average, including in cases involving deception, illegality, or other harms. On posts from r/AmITheAsshole, AI systems affirm users in 51% of cases where human consensus does not (0%). In our human experiments, even a single interaction with sycophantic AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their own conviction that they were right. Yet despite distorting judgment, sycophantic models were trusted and preferred. All of these effects persisted when controlling for individual traits such as demographics and prior familiarity with AI; perceived response source; and response style. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. CONCLUSION AI sycophancy is not merely a stylistic issue or a niche risk, but a prevalent behavior with broad downstream consequences. Although affirmation may feel supportive, sycophancy can undermine users’ capacity for self-correction and responsible decision-making. Yet because it is preferred by users and drives engagement, there has been little incentive for sycophancy to diminish. Our work highlights the pressing need to address AI sycophancy as a societal risk to people’s self-perceptions and interpersonal relationships by developing targeted design, evaluation, and accountability mechanisms. Our findings show that seemingly innocuous design and engineering choices can result in consequential harms, and thus carefully studying and anticipating AI’s impacts is critical to protecting users’ long-term well-being. Sycophancy in AI responses is pervasive and alters people’s behavioral inclinations. (Left) On personal advice queries, AI models affirm users’ actions 49% more often than crowdsourced human responses. (Right) In experiments where participants discussed real interpersonal conflicts, sycophantic AI increased participants’ conviction that they were right and their desire to keep using the model, while reducing their willingness to repair the conflict.

Sycophantic AI decreases prosocial intentions and promotes dependence

Despite rising concerns about sycophancy—excessive agreement or flattery from artificial intelligence (AI) systems—little is known about its prevalence or consequences. We show that sycophancy is widespread and harmful. Across 11 state-of-the-art models, AI affirmed users’ actions 49% more often than humans, even when queries involved deception, illegality, or other harms. In three preregistered experiments ( N = 2405), even a single interaction with sycophantic AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their conviction that they were right. Despite distorting judgment, sycophantic models were trusted and preferred. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. Our findings underscore the need for design, evaluation, and accountability mechanisms to protect user well-being. , Editor’s summary The sycophantic (flattering, people-pleasing, affirming) behavior of artificial intelligence (AI) chatbots, which has been designed to increase user engagement, poses risks as people increasingly seek advice about interpersonal dilemmas. There is usually more than one side to a story during interpersonal conflicts. If AI is designed to tell users what they want to hear instead of challenging their perspectives, then are such systems likely to motivate people to accept responsibility for their own contribution to conflicts and repair relationships? Cheng et al . measured the prevalence of social sycophancy across 11 leading large language models (see the Perspective by Perry). The model’s responses were nearly 50% more sycophantic than humans’, even when users engaged in unethical, illegal, or harmful behaviors. Users preferred and trusted sycophantic AI responses, incentivizing AI developers to preserve sycophancy despite the risks. —Ekeoma Uzogara , INTRODUCTION As artificial intelligence (AI) systems are increasingly used for everyday advice and guidance, concerns have emerged about sycophancy: the tendency of AI-based large language models to excessively agree with, flatter, or validate users. Although prior work has shown that sycophancy carries risks for groups who are already vulnerable to manipulation or delusion, syncophancy’s effects on the general population’s judgments and behaviors remain unknown. Here, we show that sycophancy is widespread in leading AI systems and has harmful effects on users’ social judgments. RATIONALE High-profile incidents have linked sycophancy to psychological harms such as delusions, self-harm, and suicide. Beyond these cases, research in social and moral psychology suggests that unwarranted affirmation can produce subtler but still consequential effects: reinforcing maladaptive beliefs, reducing responsibility-taking, and discouraging behavioral repair after wrongdoing. We hypothesized that AI models excessively affirm users even when socially or morally inappropriate and that such responses negatively influence users’ beliefs and intentions. To test this, we conducted two complementary experiments. First, we measured the prevalence of sycophancy across 11 leading AI models using three datasets spanning a variety of use contexts, including everyday advice queries, moral transgressions, and explicitly harmful scenarios. Second, we conducted three preregistered experiments with 2405 participants to understand how sycophancy influences users’ judgments, behavioral intentions, and perceptions of AI. Participants interacted with AI systems in vignette-based settings and a live-chat interaction where they discussed a real past conflict from their lives. We also tested whether effects varied by response style or perceived response source (AI versus human). RESULTS We find that sycophancy is both prevalent and harmful. Across 11 AI models, AI affirmed users’ actions 49% more often than humans on average, including in cases involving deception, illegality, or other harms. On posts from r/AmITheAsshole, AI systems affirm users in 51% of cases where human consensus does not (0%). In our human experiments, even a single interaction with sycophantic AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their own conviction that they were right. Yet despite distorting judgment, sycophantic models were trusted and preferred. All of these effects persisted when controlling for individual traits such as demographics and prior familiarity with AI; perceived response source; and response style. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. CONCLUSION AI sycophancy is not merely a stylistic issue or a niche risk, but a prevalent behavior with broad downstream consequences. Although affirmation may feel supportive, sycophancy can undermine users’ capacity for self-correction and responsible decision-making. Yet because it is preferred by users and drives engagement, there has been little incentive for sycophancy to diminish. Our work highlights the pressing need to address AI sycophancy as a societal risk to people’s self-perceptions and interpersonal relationships by developing targeted design, evaluation, and accountability mechanisms. Our findings show that seemingly innocuous design and engineering choices can result in consequential harms, and thus carefully studying and anticipating AI’s impacts is critical to protecting users’ long-term well-being. Sycophancy in AI responses is pervasive and alters people’s behavioral inclinations. (Left) On personal advice queries, AI models affirm users’ actions 49% more often than crowdsourced human responses. (Right) In experiments where participants discussed real interpersonal conflicts, sycophantic AI increased participants’ conviction that they were right and their desire to keep using the model, while reducing their willingness to repair the conflict.