R0044/2026-04-01/Q002/H1¶
Statement¶
Extensive empirical evidence exists documenting measurable harm from AI systems that agree with users rather than challenge them, including specific incident reports and case studies from high-stakes professional domains.
Status¶
Current: Eliminated
Supporting Evidence¶
| Evidence | Summary |
|---|---|
| SRC01-E01 | Science paper documents measurable behavioral changes from sycophantic AI interaction |
| SRC04-E01 | Nature study documents false confirmation errors in AI medical decision-making |
Contradicting Evidence¶
| Evidence | Summary |
|---|---|
| SRC02-E01 | Georgetown brief catalogs harm categories but most are consumer/personal, not professional domain incidents |
| SRC05-E01 | National security study documents automation bias rates but not sycophancy-specific incidents |
Reasoning¶
While significant empirical evidence exists (especially the Sharma et al. 2026 Science paper), it does not constitute "extensive" evidence of harm in high-stakes professional domains specifically. The strongest evidence is from controlled experiments, not field incident reports. H1 is too strong a claim.
Relationship to Other Hypotheses¶
H2 is the better fit — substantial lab evidence exists but field-level documentation of sycophancy-caused harm in professional settings is sparse. H3 is too pessimistic given the empirical studies that do exist.