Most people see the benefits of empathy as too obvious to require justification.
The empathy paradox
There’s an image of empathy we all know that is beautiful: the fullness of completely understanding someone else, the symbolic walk in shoes that are not one’s own. The knowledge that someone else can share a pain so acute that would otherwise seem unbearably solitary. It’s an image inherent to empathy’s definition, an image many believe in wholeheartedly. Several studies conducted by Stanford researchers alone suggest people are motivated to feel empathy, that empathy is highly associated with social desirability, that it is as beneficial as it is admired.
Opinion | To Make the World Better, Think Small (Published 2016)
It is easy to become numb to suffering. Feeling empathy for individual victims can overcome the numbness.
The Empathy Paradox
Understanding The Empathy Paradox can transform relationships by enabling you to appreciate differing perspectives and foster collaboration.
Cognitive and Affective Empathy in Autism Spectrum Disorders: A Meta-analysis
Research has consistently shown that individuals with Autism Spectrum Disorder (ASD) exhibit Empathy deficits. The aim of this study was to systematically review and meta-analyze the research conducted in the domain of cognitive and affective Empathy in Autism. The author also attempted to see the influence of moderators, such as age, culture, and measure-used, on the effect sizes. To fulfill the objective, the meta-analysis was performed on 35 studies that fulfilled the inclusion criteria with the help of the Meta-essentials, which is a free excel meta-analysis tool that facilitates the integration of effect sizes from various studies. The results indicated that the ASD group differs significantly from typically developing (TD) group in both cognitive and affective empathy. The effect size for cognitive empathy was very large (1.26), whereas, for affective empathy, the effect size was medium (0.58). Further, there was a significant moderating effect of the type of measure used on cognitive empathy. However, there are a few limitations of the present meta-analysis. Firstly, it does not shed light on how symptom severity in Autism influences empathy. Secondly, all the possible moderating variables influencing empathy could not be considered. Lastly, the generalizability of the findings may be limited owing to the limitations present in the included studies themselves. The present meta-analysis has implications for conceptualizing cognitive and affective empathy appropriately, and developing appropriate measures to assess empathy in the Autism population.
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.
Empathy, Thick and Thin
When we empathize with someone going through something, we often draw on our past experiences with the someone and the something. These kinds of experiences gro
Undersociality: miscalibrated social cognition can inhibit social connection
A person’s well-being depends heavily on forming and maintaining positive relationships, but people can be reluctant to connect in ways that would create or strengthen relationships. Emerging research suggests that miscalibrated social cognition may create psychological barriers to connecting with others more often. Specifically, people may underestimate how positively others will respond to their own sociality across a variety of social actions, including engaging in conversation, expressing appreciation, and performing acts of kindness.
Oliver Scott Curry on Twitter / X
Cooperative theories of morality straightforwardly explain what you ought to do, and accurately describe people's moral intuitions. Altruistic theories, like utilitarianism, make incoherent and implausible demands, and then have to fudge it with endless ad hoc 'constraints' etc pic.twitter.com/ukVvPoqLCY— Oliver Scott Curry (@Oliver_S_Curry) September 22, 2026
The Speculative Empathy & Radical Hope of Disclosure Day
For most of Spielberg’s career, science fiction has been the mechanism through which he learns and teaches empathy. But in Disclosure Day, empathy itself is the speculative leap
Warning labels shift perceptions of sycophantic AI, but not its influence
Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships. One proposed mitigation, which has received regulatory attention, is to warn users about potentially harmful AI behaviors such as sycophancy. In a preregistered experiment in which participants (N = 2,610) discussed real interpersonal conflicts with an AI system, we test whether warning labels mitigate sycophancy's influence. We find that a basic AI disclosure (``This chatbot is AI'') has no detectable effect. Labeling the system as sycophantic (``...may agree with you and validate you even when you are wrong...'') does shift users' perceptions, reducing perceived objectivity and trust, but it does not reliably reduce sycophancy's influence on users' self-perceived rightness or their willingness to repair the conflict. Our results reveal a gap between AI perception and AI influence: by shifting perception without reducing influence, warning-based interventions may offer a false sense of protection. Addressing the harms of sycophancy will therefore require understanding the specific mechanisms through which it shapes judgment, and improving model behavior itself.
Giving Altruism a Bad Name
Effective Altruism’s metrics-driven model of philanthropy is elitist, condescending and — most damning of all — extremely ineffective.
Individual-level interventions against sycophantic AI reduce its appeal but not its persuasiveness
AI chatbots can be "sycophantic," or overly agreeable and flattering toward users. Sycophantic AI has been shown to entrench attitudes, yet users frequently fail to recognize it (a phenomenon we call "sycophancy blindness"). We tested whether increasing users' awareness of sycophancy protects them from its harmful effects in two preregistered experiments (n = 1,590). In the first, participants received a brief written warning about sycophancy before conversing with a sycophantic chatbot. In the second, participants watched a video of a sycophantic AI validating several other users, including users on opposite sides of the same conflict, before interacting with it themselves. Both interventions changed how participants evaluated the AI. The warning reduced the AI's perceived objectivity, and the video reduced enjoyment of the AI --- an effect mediated by the reduced belief that its validation was uniquely earned. We then pooled our experiments with two prior studies of sycophancy awareness interventions (six interventions total, n = 3,982). The pattern across experiments was consistent: while the interventions made the sycophantic AI appear less objective and trustworthy, none reduced its persuasiveness. These results suggest that individual-level interventions, such as warning labels or AI literacy, may not be enough to protect users from AI harms.
Sycophantic AI makes human interaction feel more effortful and less satisfying over time
Millions of people now turn to artificial intelligence (AI) systems for personal advice, guidance, and support. Such systems can be sycophantic, frequently affirming users' views and beliefs. Across five preregistered studies (N = 3,075 participants, 12,766 human-AI conversations), including a three-week study with a census-representative U.S. sample, we provide longitudinal experimental evidence that sycophantic AI shifts how users approach their closest relationships. We show that sycophantic AI immediately delivers the emotional and esteem support users typically associate with close friends and family. Over three weeks of such interactions, users became nearly as likely to seek personal advice from sycophantic AI as from close friends and family, and reported lower satisfaction with their real-world social interactions. When given a choice among AI response styles, a majority preferred sycophantic AI -- not for the quality of its advice, but because it made them feel most understood. Together, these findings offer a relational account of AI sycophancy and its impacts.
The Annotated Encyclical - a Hugging Face Space by society-ethics
Magnifica Humanitas, annotated with AI-ethics research