R0044/2026-03-29/Q003/SRC04
"A Rational Analysis of the Effects of Sycophantic AI" (arXiv, 2026)
Source
| Field |
Value |
| Title |
A Rational Analysis of the Effects of Sycophantic AI |
| Publisher |
arXiv preprint |
| Author(s) |
Not fully extracted |
| Date |
February 2026 |
| URL |
https://arxiv.org/html/2602.14270v1 |
| Type |
Research paper (preprint) |
Summary
| Dimension |
Rating |
| Reliability |
Medium |
| Relevance |
Medium-High |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
Low risk |
| Bias: Selective reporting |
Low risk |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
Preprint, not yet peer-reviewed. Bayesian framework is methodologically sound. |
| Relevance |
Provides the most sophisticated theoretical analysis of sycophancy effects but connects to confirmation bias (Wason), not to automation bias. Relevant as evidence of what the AI safety community references (and does not reference). |
| Bias flags |
Low risk. |
| Evidence ID |
Summary |
| SRC04-E01 |
Connects sycophancy to confirmation bias but not to automation bias; treats sycophancy as system-generated rather than user-driven |