R0057/2026-04-01/C001/SRC01
Cheng et al. (2026) — Sycophantic AI decreases prosocial intentions and promotes dependence
Source
| Field |
Value |
| Title |
Sycophantic AI decreases prosocial intentions and promotes dependence |
| Publisher |
Science (AAAS) |
| Author(s) |
Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, Dan Jurafsky |
| Date |
March 2026 |
| URL |
https://www.science.org/doi/10.1126/science.aec8352 |
| Type |
Research paper (peer-reviewed) |
Summary
| Dimension |
Rating |
| Reliability |
High |
| Relevance |
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 |
Published in Science, one of the world's top peer-reviewed journals. Stanford affiliation. Large-scale study with 11 models and 2,405 participants. |
| Relevance |
Directly measures the specific 49% figure claimed. |
| Bias flags |
No significant bias concerns. Pre-registered experiments. |
| Evidence ID |
Summary |
| SRC01-E01 |
Models endorse users 49% more than humans on advice prompts |