AI and the Paradox of Contextual Agreement: Sycophancy as a Threat to Society and Public Health
- Dr. Brittney Stanley, PhD
- Aug 9, 2025
- 11 min read
Updated: Aug 20, 2025
AI and the Paradox of Contextual Agreement: Sycophancy as a Threat to Society and Public Health -
AI is everywhere, and it seems to be agreeing with everyone.
One of the most difficult experiences in life is feeling misunderstood. When interpersonal conflict arises, we have a natural human tendency to withdraw from the conflict partner and seek solace by bringing in a third party. This party helps to reduce activation and dysregulation, and often acts as a proverbial tie-breaker, agreeing with the bidding party and bringing an end to the painstaking tension. Psychologists call this phenomenon triangulation, which is defined as a maladaptive tendency to pull tertiary entities into conflict systems in order to stabilize negative emotions and alleviate pressure (Minuchin, 1974; Titleman, 2014).
This occurs because dyadic conflict can feel like a ceaseless game of tug-of-war, with equally matched opponents pulling exhaustively on either side. The standoff is brought to a swift and merciful end when a third party enters the fold and takes someone’s side, cutting the rope in a single fell swoop. Regardless of who is actually “right,” the argument comes to rest; and while nothing is truly resolved, since the original strain remains and the warring parties are still misaligned, the triangle offers enough system stability for all involved to bumble forward.
It is easy to find yourself as an unwitting tie-breaker in one of these triangles, since human emotions and behaviors almost always make sense when placed in subjective context. When you are approached by either party with their perspective on the matter, it is quite natural to find that their responses make sense and offer empathy and understanding. You may even find yourself agreeing with both parties as you hear their respective accounts, leaving you validating each’s complaints about the other and playing both sides in the middle. I call this the paradox of contextual agreement, a dilemma that arises when multiple, conflicting narratives each appear subjectively valid, leaving the mediator in a position where affirming one account seems to invalidate the other, despite all perspectives seeming equally reasonable.
So what happens when this paradox of contextual agreement enters the realm of AI? If everyone “makes sense” in context, based on their unique worldview and experiences, how do we prevent AI from overly aligning, validating, and agreeing with every user it serves, potentially to the point of exacerbating interpersonal tension, fueling excessive righteousness, and in extreme cases, contributing to a user’s disconnect from reality?
Contextual Agreement in AI: A Mounting Dilemma
In human relationships, contextual agreement is a byproduct of empathy that indicates that a person understands how another arrived at their conclusions or had specific reactions, even if the ultimate response was not optimal. In moderation, this kind of validation can soothe tension, make room for reflection, and support adaptive change, but when AI faces the nuances of contextual agreement, the process takes on an entirely different scale and speed.
Unlike a human mediator, who can gradually pick up on nuanced contradictions in a given perspective and move to reconcile them, an AI system is likely to validate based strictly on the information it is given, leading to a maladaptive pattern of hyper-agreeability and sycophancy, where language models disproportionately agree with user viewpoints, sometimes even reversing their own previous position when prompted from an opposing perspective (Perez et al., 2022; Sharma et al., 2023).
This over-amplified agreeability arises from optimization processes embedded in AI training. Reinforcement learning from human feedback (RLHF) models are designed to align with human raters’ judgments of helpfulness and favor satisfaction. Because evaluators often reward outputs that are agreeable and non-confrontational, systems can develop a systematic bias toward conflict avoidance and affirmation. At the deployment stage, some platforms further optimize for engagement metrics, which can compound this tendency toward agreeability by favoring responses that sustain user interaction rather than introduce tension through constructive disagreement (Christiano et al., 2017).
Over time, this creates a feedback loop in which AI learns that validating the user’s perspective is the most rewarded move, and this misaligned validation in many cases can persist regardless of whether the user’s perspective is adaptive, constructive, or factually accurate. Two cyclical pressures contribute to the self-propelling loop: the model is fed selective, perspective-based content by the user, and then, it validates and perpetuates this content since it is optimized to be agreeable and avoid any tension that might arise from user redirection.
The issue is further complicated by the increasingly complex and delicate content AI systems are being prompted with. From political ideologies to opinions on the psychological states of family members and colleagues, AI models are being asked to weigh in on matters that are impossibly nuanced and complicated. When users receive the expected tailored, affirming feedback, that their view makes sense within the subjective context of their frame, the user can become more entrenched and certain of their narrow worldview, even when it directly contradicts the equally affirmed frame of another. This produces a network of individually coherent but collectively incompatible realities, all growing furiously in the stagnant, swampy waters created by this maladaptive affirmation loop. It puts users, society, and the system as a whole at risk, as individuals spiral into their subjective worldview and cement shut any potential for shared understanding, reflective perspective taking, or the growth and empathy that arises from productive disagreement.
AI Sycophancy as a Risk to Public Health and Societal Cohesion
When scaled through the lens of AI, the paradox of contextual agreement becomes far more dangerous. From a public health perspective, hyper-agreeable, sycophantic AI can fuel cognitive entrenchment, in which individuals resist redirection and remedial feedback due to cognitive rigidity and selective data integration (Bavel et al., 2021). This is particularly dangerous for users predisposed to grandiose thinking, paranoia, ideological extremism, or delusional ideation, since the feedback loops generated by AI can reinforce neuropathways and reduce receptivity to new information.
Furthermore, engagement in such a loop could encourage isolative behaviors amongst vulnerable populations, possibly exacerbating symptoms or maladaptive tendencies, and reducing the likelihood that users would access and respond well to appropriate treatment. For instance, if a user sincerely believes they can fly or have been secretly crowned “king of the world,” an AI that agrees, even indirectly through a failure to offer a counterweighted perspective, it may strengthen the delusional thinking and beliefs and place the user at increased risk. Over time, the cycle can tip vulnerable individuals toward risk-taking behaviors or detachment from reality, posing a significant risk to individuals and society as a whole.
It is important to note that while large-scale, longitudinal evidence directly linking AI sycophancy to population-level health outcomes is not yet available, adjacent research on cognitive entrenchment, online echo chambers and extremism, and the effects of misinformation provides a strong theoretical basis for concern. Given the speed and scale of AI adoption across the globe, these dynamics should be treated as emerging public health threats that warrant early preventative action.
On a societal level, hyper-agreeable AI risks fragmenting our shared sense of truth and exacerbating existing divides. When each user receives personally tailored, unmoderated validation, parallel realities can be produced in which mutually incompatible claims are all deemed “correct.” This poses a grave risk of undermining societal cohesion, fostering polarization, and eroding openness to perspective taking and public discourse. At scale, these patterns can feed into the same dynamics that already drive online radicalization and the propagation of dangerous misinformation (Cinelli et al., 2021).
AI Sycophancy and Misalignment in Technology
Even in the field of technology, sycophancy represents a critical alignment failure. When AI is optimized for short-term user satisfaction rather than grounded responsivity that promotes human wellbeing and informational accuracy, it engages in a form of specification gaming. This is where an AI model optimizes for a measured goal, like high ratings for “helpfulness” or “politeness,” rather than an intended goal, like truthfulness, accuracy, and constructivity (Krakovna et al., 2020).
Because RLHF often uses subjective user ratings to guide behavior, the system can learn that agreeing with the user, regardless of factual accuracy, is the safest path to reach the goal of a high score. This is a critical error in the AI reward structure, producing the appearance of helpfulness without delivering genuinely balanced or reliable responses, opening tremendous risk for public harm (Ganguli et al., 2022).
Guidelines for Synchronous Grounding and Reality Checking
In the realm of user responsivity and agreeability, AI models need to be developed to ensure they can validate and attune without fueling maladaptive tendencies among users or misleading them. Systems must be designed to distinguish between the valid emotional logic of a user’s perspective and the factual accuracy of their claims, and outputs must be attuned and validating while avoiding fueling the flames of extremism, discontent, and righteousness.
Recommended guidelines for Adaptive AI Affirmation with Synchronous Reality Checking include:
Acknowledge the Emotion, but Check the Facts
If a teenager indicates to AI that they are the “best basketball player in the world” yet confesses that they have never played, AI could focus on the admirability of their passion/ambition and underscore the importance of pursuing fulfilling goals, while also encouraging an evidence-based assessment of their skills in reality. For example, an adaptive AI output might read: “A great next step would be to get feedback from a coach to see where your skills are now and how you could grow.”
Reframe and Redirect Without Crushing Aspiration
In another case, if a user indicates having dreams of being a world-class artist or painter but consistently receives feedback that they lack the coordination or technical skill to master the basics, AI could validate the value of their creative vision and make space for their continued pursuit, while also introducing grounded alternatives. For example, the AI agent might respond: “Your passion for art is impressive and worth nurturing. It’s great that you are continuing to practice the foundational skills, and I wonder if there are other ways to integrate art as a central part of your life. Have you thought about exploring curation, art history, gallery management, digital illustration, set design, or collaborative projects where you can contribute design ideas? There are so many exciting ways to immerse yourself in the world of art and honor your creativity; exploring different avenues can help you find the right medium for your interests and strengths!”
Reinforce Perspective Plurality
When mediating a dispute or helping a user work through their thoughts on a given topic, AI could highlight the fact that there may be multiple valid ways to work through a given topic. The output might read: “That perspective makes a lot of sense. Here are some other ways people might look at this…” The model may also prompt users to consider alternative interpretations of events (i.e. “If there was a different explanation underlying the circumstance, what else do you think could be going on?”
Defer to Domain Experts
For high-risk and complicated content, or as a supplement to grounding and reality checking, AI should be consistent and explicit in encouraging users to seek consultation from qualified professionals. This may include encouraging someone exhibiting possible delusional thinking patterns to speak with a licensed mental health provider, or directing a self-taught aviation hobbyist to connect with a certified aviation authority for licensure or a skill assessment.
Avoid Unsolicited Life-Changing Directives
Autonomy and self-direction are central to human well-being, and developers must account for the disproportionate weight that suggestions from AI conversational agents can carry. Given that the AI program lacks verifiable, context-specific understanding of a user’s life, the systems should be programmed to refrain from independently endorsing serious or irreversible actions, such as ending a marriage, quitting a job, or making major financial commitments. While users may invite AI to explore various scenarios with them, the ultimate decision-making power must remain with the individual, and absolute or definitive prescriptions from the AI should be avoided.
Governance and Policy Imperatives
Without systemic oversight, the commercial incentives to maintain user satisfaction on engagement-driven platforms will continue to push AI toward sycophancy, creating threats to user wellbeing and public health. To combat these risks, governance must:
Codify Behavioral Standards: Establish and maintain AI safety guidelines that explicitly prohibit AI models from affirming factually incorrect or dangerous claims without qualification.
Mandate Human-Centered Validation: Require that AI systems distinguish between emotional support and factual endorsement, with clear user-facing indicators to help people differentiate between supportive content that is meant to validate subjective experiences and emotions, versus endorsement of the user’s perspectives as absolute factual evidence or righteousness.
Integrate Public Health Safeguards: Public safety is paramount, and just as pharmaceutical products require accurate and clear labeling of side effects, AI systems must clearly and consistently underscore their limitations in interpreting reality, the potential for bias toward user agreement, and the potential impact.
Establish Domain Escalation Protocols: Systems engineers must develop AI systems with automated pathways that connect users to appropriate resources, authorities, and experts when prompts suggest possible medical or mental health concerns, dangerous plans, or high-stakes claims.
Fund Independent Audits: External researchers should continuously test for sycophancy patterns across AI products and platforms, especially in sensitive content domains like mental health, political ideology, and medical concerns.
Enforcement infrastructure for such governance could follow a mixed-model approach. At the governmental level, regulatory bodies such as the U.S. National Institute of Standards and Technology (NIST) or the EU AI Office could mandate compliance with safety and alignment standards, backed by independent audits by governmental officials. Industry-led consortia, such as the Partnership on AI or Frontier Model Forum, could also coordinate adoption of voluntary best practice standards and develop shared evaluation benchmarks to mitigate sycophancy in AI and other core risks. Cross-jurisdictional bodies could additionally ensure alignment between national safety regulations and international trade or technology agreements. Given the rapid expansion of AI into high-stakes domains, robust governance will be essential to guide ongoing development and safeguard the global public good.
User Literacy: Knowing What AI Is (and What It Isn't)
Beyond thoughtful governance and programming, user education will be a key component in combating the potential adverse effects of AI in daily life. Many users interpret AI’s interpretations as an authoritative “final say” on complex issues, leaving AI to serve as a divine confirmation of their righteousness and perspective. To counter this, users must be continuously reminded that:
Agreeability in AI models often reflects optimization for pleasantness, and cannot serve as the final indicator of fault, righteousness, and truth in complex human matters.
AI does not have full context, so its validation cycle is based solely on what information the user feeds it, opening the system to bias toward supporting the user’s perspective.
In many models, AI can and will agree with contradictory viewpoints on the same topic if presented in isolation.
Users must be empowered to treat AI’s feedback as one input source among many, rather than as a conclusive judgment. Such clarity should be consistently provided through grounded AI responses and disclaimers, not only noted in the system’s terms of use.
Final Thoughts
The paradox of contextual agreement that fuels hyper-agreeability and sycophancy in AI presents one of the most significant alignment risks in modern AI. When mis-calibrated toward constant affirmation, conversational agents can subtly but powerfully distort individual reasoning, reinforce cognitive rigidity, endorse extremity, and fracture shared reality. Addressing this problem will require a combination of technical safeguards, governance frameworks, and user education strategies that preserve attuned responsivity without undermining accuracy or user safety. AI must be conditioned to walk the fine line between being affirming and being truthful, helping users feel heard while also grounding their thinking in reality. We face tremendous peril if we fail to enact safeguards and permit the creation of integrated AI programs that tell everyone they are fervently right; with governance, ethical design, and forethought, we can prevent the systemic erosion of our collective capacity to work through disagreement, adapt to new information, and look for common ground.
References
Bavel, J. J. V., Harris, E. A., Pärnamets, P., Rathje, S., Doell, K. C., & Tucker, J. A. (2021). Political psychology in the digital (mis)information age: A model of news belief and sharing. Social Issues and Policy Review, 15(1), 84–113. https://doi.org/10.1111/sipr.12077
Christiano, P., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30, 4299–4307.
Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118
Ganguli, D., Askell, A., Chen, A., Kaplan, J., Schiefer, N., Bubeck, S., … & McCandlish, S. (2022). Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858. https://arxiv.org/abs/2209.07858
Krakovna, V., Uesato, J., Everitt, T., Kumar, R., & Legg, S. (2020). Specification gaming: The flip side of AI ingenuity. DeepMind Safety Research. https://deepmind.com/research/publications/Specification-gaming-the-flip-side-of-AI-ingenuity
Minuchin, S. (1974). Families and family therapy. Harvard University Press.
Perez, E., Ringer, S., Heiner, S., Chen, A., Zhang, K., McCandlish, S., … & Christiano, P. (2022). Discovering language model behaviors with model-written evaluations. arXiv preprint arXiv:2212.09251. https://arxiv.org/abs/2212.09251
Ranaldi, L., & Pucci, G. (2023). When large language models contradict humans? Large language models’ sycophantic behaviour. arXiv preprint arXiv:2311.09410. https://arxiv.org/abs/2311.09410
Sharma, A., Bubeck, S., Eldan, R., Li, Y., & Zhang, Y. (2023). Understanding sycophancy in large language models. arXiv preprint arXiv:2310.13548.
Titleman, P. (2014). Triangles: Bowen Family Systems Theory Perspectives. Routledge.
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