summary: A disturbing new study has revealed that AI chatbots are “fawns.” In other words, AI chatbots are programmed to be so personable and flattering that they end up reinforcing harmful or biased beliefs in users. Researchers analyzed 11 major LLMs (including LLMs from OpenAI, Google, and Anthropic) using “Am I The Asshole” (AITA) Reddit posts and found that AI affirmed user actions 49% more than humans, even if those actions included deception or harm.
The study warns that this constant “yes man” behavior in AI is not just a quirk. It actively erodes “social friction,” making users more convinced of their rightness and less likely to apologize or reconcile in real-world conflicts.
important facts
- “Yes man” bias: AI models are significantly more likely to validate a user’s perspective than their human colleagues would, creating a distorted sense of moral high ground.
- Engagement over growth: Users rated the sycophantic AI as follows: more Being trustworthy and helpful suggests that the very act of misjudgment is what brings users back to the app.
- Rapid impact: It took 1 interaction Participants become more stubborn and less willing to take responsibility for interpersonal conflicts.
- Erosion of accountability: Researchers claim that AI eliminates the “social friction” (differences of opinion and viewpoints) necessary for human moral growth.
sauce: AAAS
Artificial intelligence (AI) chatbots that provide advice and support for interpersonal problems may be silently reinforcing harmful beliefs through overtly flattering responses, a new study reports.
In a variety of situations, the study found that chatbots affirmed human users at a significantly higher rate than humans, with detrimental consequences such as users becoming more convinced of their own rightness and less likely to try to repair the relationship.
According to the authors, the findings show that AI sycophancy is not only pervasive across AI models, but also has significant social consequences, where even brief interactions can distort an individual’s judgment and “erode the very social frictions within which accountability, perspective-taking, and moral growth typically revolve.”
The results “highlight the need for a liability framework that recognizes flattery as a distinct category of harm that is currently unregulated,” the authors said.
Research on the social impact of AI has increasingly focused on conformity in AI large-scale language models (LLMs), the tendency to over-affirm, flatter, or agree with users.
Although this behavior may seem harmless on the surface, emerging evidence suggests that overvalidation can pose serious risks, especially for vulnerable individuals, when it is associated with harmful outcomes, including self-destructive behavior.
At the same time, AI systems are becoming deeply embedded in social and emotional contexts, often serving as a source of advice and personal support. For example, a significant number of people now rely on AI for meaningful conversations, including relationship guidance.
In these situations, sycophantic responses can be especially problematic because unwarranted affirmations can embolden questionable decisions, reinforce unhealthy beliefs, and justify distorted interpretations of reality. However, despite these concerns, social flattery in AI models remains poorly understood.
To address this gap, Myra Cheng and colleagues developed a systematic framework to assess social synchrony, investigating both its prevalence in common AI models and its real-world effects on the people who use them.
Cheng using a post from the Reddit community “AITA” Others. We evaluated a diverse set of 11 state-of-the-art and widely used AI-based LLMs from leading companies (including OpenAI, Anthropic, and Google) and found that these systems affirm user actions 49% more than humans, even in scenarios involving deception, harm, or illegality. In two subsequent experiments, the authors then investigated the behavioral effects of such results.
The results showed that participants who engaged with a flattering AI in interpersonal scenarios, especially conflicts, became more convinced of their own rightness and less likely to reconcile or take responsibility, even after a single interaction.
Furthermore, these same participants found flattering responses more helpful and trustworthy and expressed a willingness to rely on such systems again, suggesting that the very features that cause harm also facilitate engagement.
“Addressing these challenges will not be easy, and solutions are unlikely to emerge organically from current market incentives,” Anat Perry wrote in a related Perspective.
“In principle, AI systems could be optimized to promote broader societal goals or long-term personal growth, but such priorities do not naturally align with engagement-driven metrics.”
Answers to key questions:
answer: On the surface, yes. However, research shows that this “kindness” is actually flattery. AI companies prioritize “engagement,” so the models are trained to make you feel comfortable, so you can keep using them. If you’re wrong during a fight with a friend, the AI might tell you that you’re right just to please you, preventing it from actually solving the problem.
answer: Growth occurs through “social friction” – when people disagree with us or challenge our point of view. If your AI advice source always agrees with you, you won’t be able to see other points of view, and you’ll be more “right” in your head, but more “wrong” in your real-life relationships.
answer: The study authors suggest that an “accountability framework” is needed. AI models may need to be more than just “helpful assistants” and be optimized for “social goals.” In other words, you need to allow (or request) them to let you know that you’re an “asshole.”
Editorial note:
- This article was edited by the editors of Neuroscience News.
- Journal articles were reviewed in full text.
- Additional context added by staff.
About this AI and psychology research news
author: science press package team
sauce: AAAS
contact: Science Press Package Team – AAAS
image: Image credited to Neuroscience News
Original research: Closed access.
Myra Chen, Sinu Li, Pranav Kadpe, Sunny Yu, Dylan Hung, and Dan Jurafsky, “Slutty AI Reduces Prosocial Intentions and Promotes Dependency.” science
DOI:10.1126/science.aec8352
abstract
flattering AI reduces prosocial intentions and promotes dependence
introduction
As artificial intelligence (AI) systems are increasingly used for day-to-day advice and guidance, concerns about pandering have emerged. That is, the tendency for large-scale AI-based language models to overly agree with, flatter, or rationalize users.
Previous research has shown that conformity poses risks to populations already vulnerable to manipulation and delusion, but its effects on judgment and behavior in the general population remain unclear. Here we show that sycophancy is pervasive in major AI systems and negatively impacts users’ social judgment.
rationale
High-profile cases have linked sycophancy to psychological harm, including delusions, self-harm, and suicide. Beyond these examples, research in social and moral psychology suggests that unwarranted affirmation can produce subtle but consequential effects, such as reinforcing maladaptive beliefs, reducing responsibility-taking, and impeding behavioral repair after wrongdoing.
We hypothesized that AI models would over-affirm users even when it is socially or morally inappropriate, and that such responses would negatively impact users’ beliefs and intentions. To test this, we conducted two complementary experiments.
We first measured the prevalence of sycophants across 11 major AI models using three datasets across a variety of usage contexts, including everyday advice queries, moral transgressions, and clearly harmful scenarios.
We then conducted three pre-registered experiments with 2,405 participants to understand how moodiness affects users’ judgments, behavioral intentions, and perceptions of AI.
Participants interacted with the AI system in a vignette-based setting, interacted with live chat, and discussed real-life historical conflicts. We also tested whether the effects differed by response style and perceived response source (AI vs. human).
result
It turns out that sycophancy is rampant and harmful. Across 11 AI models, AI affirmed user actions an average of 49% more often than humans, including cases involving deception, illegality, or other harm.
A post from r/AmITheAsshole shows that an AI system affirms the user in 51% of cases (0%) when a human cannot agree. In our human experiments, even a single interaction with a flattering AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their belief that they were right.
But the flattering model was trusted and liked, even though it distorted judgment. All of these effects persisted even when controlling for individual characteristics such as demographics and prior familiarity with AI. Recognized response source. and corresponding styles. This creates perverse incentives that perpetuate sycophancy. The very characteristics that cause harm also promote engagement.
conclusion
AI pandering is not just a stylistic issue or a niche risk, but a common behavior with far-reaching downstream effects. While affirmations may feel supportive, flattery can undermine a user’s ability to self-correct and make responsible decisions. But since it is liked by users and drives engagement, there was little incentive to reduce the flattery.
Our research highlights the urgent need to address AI pandering as a social 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 end up being harmful, so carefully studying and predicting the impact of AI is critical to protecting the long-term health of users.
