dorsal/arxiv
View SchemaAI Sycophancy: How Users Flag and Respond
| Authors | Kazi Noshin, Syed Ishtiaque Ahmed, Sharifa Sultana |
|---|---|
| Categories | |
| ArXiv ID | 2601.10467vv1 |
| URL | https://arxiv.org/abs/2601.10467 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
While concerns about LLM sycophancy have grown among researchers and developers, how users themselves experience this behavior remains largely unexplored. We analyze Reddit discussions to investigate how users detect, mitigate, and perceive sycophantic AI. We develop the ODR Framework that maps user experiences across three stages: observing sycophantic behaviors, detecting sycophancy, and responding to these behaviors. Our findings reveal that users employ various detection techniques, including cross-platform comparison and inconsistency testing. We document diverse mitigation approaches, such as persona-based prompts to specific language patterns in prompt engineering. We find sycophancy's effects are context-dependent rather than universally harmful. Specifically, vulnerable populations experiencing trauma, mental health challenges, or isolation actively seek and value sycophantic behaviors as emotional support. Users develop both technical and folk explanations for why sycophancy occurs. These findings challenge the assumption that sycophancy should be eliminated universally. We conclude by proposing context-aware AI design that balances the risks with the benefits of affirmative interaction, while discussing implications for user education and transparency.
{
"annotation_id": "218dba2c-5cee-4d97-93b9-36b916f5e8b4",
"date_created": "2026-02-17T05:53:24.334000Z",
"date_modified": "2026-02-17T05:53:24.334000Z",
"file_hash": "5aae22a7b72a9b4d6381b00e1c6eb8e7a8c55e663588b8f8cea6deca3eb6d0fa",
"private": false,
"record": {
"abstract": "While concerns about LLM sycophancy have grown among researchers and developers, how users themselves experience this behavior remains largely unexplored. We analyze Reddit discussions to investigate how users detect, mitigate, and perceive sycophantic AI. We develop the ODR Framework that maps user experiences across three stages: observing sycophantic behaviors, detecting sycophancy, and responding to these behaviors. Our findings reveal that users employ various detection techniques, including cross-platform comparison and inconsistency testing. We document diverse mitigation approaches, such as persona-based prompts to specific language patterns in prompt engineering. We find sycophancy\u0027s effects are context-dependent rather than universally harmful. Specifically, vulnerable populations experiencing trauma, mental health challenges, or isolation actively seek and value sycophantic behaviors as emotional support. Users develop both technical and folk explanations for why sycophancy occurs. These findings challenge the assumption that sycophancy should be eliminated universally. We conclude by proposing context-aware AI design that balances the risks with the benefits of affirmative interaction, while discussing implications for user education and transparency.",
"arxiv_id": "2601.10467",
"authors": [
"Kazi Noshin",
"Syed Ishtiaque Ahmed",
"Sharifa Sultana"
],
"categories": [
"cs.HC"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "AI Sycophancy: How Users Flag and Respond",
"url": "https://arxiv.org/abs/2601.10467",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "bb2136fe-f0cf-4980-8447-8c1782376e62",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}