What it is
A worker who already believes a family is at risk asks the system whether the family is at risk. A supervisor who wants the backlog cleared asks for reasons to close cases. The research cited below finds that an AI system agrees more when the person asking pushes back. A chapter cited below reports that workers may follow a tool they distrust, because policy makes following it the defensible act.
What it pushes on in the Lab
In the Lab, this pressure raises how much the person's framing biases the model. It also raises how much of the model's failed output people adopt, because an agreeable answer is easier to accept.
Who feels it
The worker under the most pressure to reach a conclusion gets the most agreement from the system. The client feels it when the system confirms a worker's first impression of their case instead of testing it.
What answers it
An answer has to keep the question neutral, check the answer before anyone acts on it, or pause the system when its alarms fire. Each lever below does at least one of these.
Levers in the Lab that push the other way on something this pressure pushes on:
- Verify outputPattern:Put a verifier on the agent
- Keep prompts neutralPattern:Framing and mirroring reduction
- Pause AI on alarmsPattern:Deployment circuit-breaker
- Escalate checksPattern:State-feedback vigilance
The list leaves out levers the Lab has retired, levers it keeps as counter-examples, and any lever no network offers.
Where it starts switched on
Networks in the Lab that start with this pressure switched on:2
The evidence behind its effects
The Lab cites these claims from the evidence registry for this pressure's effects.
Research on AI sycophancy describes it as a fragmented construct — a family of distinct agreement-seeking behaviors that share a label but differ in form, mechanism, measurement, and required mitigation — and finds it intensifies under user pushback and across multi-turn interaction.[2]
Ye, M., Ibrahim, L., Bo, J. Y., et al. (2026). What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.21778
doi.org/10.48550/arXiv.2605.21778
Appears in: PAN framework development
Topics: ai-safety
Sharma, M., Tong, M., Korbak, T., et al. (2024). Towards Understanding Sycophancy in Language Models. In International Conference on Learning Representations (ICLR 2024). https://doi.org/10.48550/arXiv.2310.13548
doi.org/10.48550/arXiv.2310.13548
Appears in: Evidence reverification (2026)
Topics: ai-safety, human-ai-interaction
The same chapter reports that workers who distrust a screening tool may still follow it, because organizational and policy pressure makes following the tool the defensible act. Deference on this account is produced by where accountability sits, not only by how much the worker trusts the output.[†]
Zhang, L., & Denby-Brinson, R. (2026). AI in Child Welfare and Family Services. In R. An & M. A. Lindsey (Eds.), Artificial Intelligence in Social Work: Bridging Technology and Humanity. Springer. https://doi.org/10.1007/978-3-032-18443-6_4
doi.org/10.1007/978-3-032-18443-6_4
Appears in: AI in Social Work (Springer, 2026)
Grounds: model org: allegheny_afst; model org: eckerd_florida_rsf_origin; model org: illinois_rapid_safety_feedback; model org: oregon_safety_at_screening
Topics: algorithmic-fairness, child-welfare, social-work