R0044/2026-03-29/Q002 — Self-Audit¶
ROBIS 4-Domain Audit¶
Domain 1: Eligibility Criteria¶
Rating: Low risk
| Criterion | Assessment |
|---|---|
| Defined before search | Yes — focused on documented consequences, case studies, empirical studies in professional contexts |
| Consistent application | Yes — same criteria across all sectors |
Notes: The distinction between "system sycophancy" and "automation bias" was not part of the original eligibility criteria but emerged as the most important analytical dimension.
Domain 2: Search Comprehensiveness¶
Rating: Some concerns
| Criterion | Assessment |
|---|---|
| Multiple search strategies | Yes — sycophancy harm, clinical automation bias, military overreliance |
| Designed to test each hypothesis | Yes — searched for both presence and absence of documented harm |
| All results dispositioned | Yes |
| Source diversity achieved | Yes — Science, JAMA, ICRC, OpenAI, ACM |
Notes: Engineering and financial services sectors yielded no sector-specific case studies. This is an absence finding. Military case studies may exist in classified sources.
Domain 3: Evaluation Consistency¶
Rating: Low risk
| Criterion | Assessment |
|---|---|
| All sources scored consistently | Yes |
| Evidence typed consistently | Yes |
| ACH matrix applied | Yes |
| Diagnosticity analysis performed | Yes |
Domain 4: Synthesis Fairness¶
Rating: Low risk
| Criterion | Assessment |
|---|---|
| All hypotheses given fair hearing | Yes — the distinction between H1 and H3 was maintained throughout |
| Contradictory evidence surfaced | Yes — SRC01/SRC02 contradict H3, noted explicitly |
| Confidence calibrated to evidence | Yes |
| Gaps acknowledged | Yes — engineering, finance, classified military |
Overall Assessment¶
Overall risk of bias: Low risk
The research process was thorough across available sectors. The key analytical contribution — distinguishing between automation bias (human over-reliance) and sycophancy (system agreeableness) as harm mechanisms — emerged from the evidence rather than from prior assumptions.
Researcher Bias Check¶
- Framing bias: The query assumes harm exists, which could bias toward confirmation. The evidence independently supports this conclusion, but the analysis maintains the distinction between proven and projected harm.
- Availability bias: The OpenAI incident is highly publicized and may receive disproportionate weight. It was weighted appropriately given its direct relevance as the only documented system-side sycophancy incident.