R0044/2026-03-29/Q002 — ACH Matrix¶
Matrix¶
| H1: Documented harm exists | H2: No documented harm | H3: Harm from automation bias, not sycophancy | |
|---|---|---|---|
| SRC01-E01: Science — 49% more affirmation, reduced prosocial behavior | ++ | -- | - |
| SRC02-E01: OpenAI GPT-4o — endorsed medication non-compliance | ++ | -- | - |
| SRC03-E01: JAMA — 31% higher misdiagnosis minorities | ++ | -- | ++ |
| SRC04-E01: Bowtie — automation bias systemic healthcare risk | + | -- | ++ |
| SRC05-E01: ICRC — military operators accept AI uncritically | + | -- | ++ |
| SRC06-E01: Marvin Project — 82% trust rate | + | -- | ++ |
Legend:
- ++ Strongly supports
- + Supports
- -- Strongly contradicts
- - Contradicts
- N/A Not applicable to this hypothesis
Diagnosticity Analysis¶
Most Diagnostic Evidence¶
| Evidence ID | Why Diagnostic |
|---|---|
| SRC02-E01 | The OpenAI incident is the most diagnostic evidence — it distinguishes between H1 and H3 because it is clearly system-side sycophancy (not human over-reliance), weakening H3's "only automation bias" claim |
| SRC03-E01 | The JAMA evidence is the most diagnostic for H3 — it clearly shows harm from human deference, not system agreeableness |
Least Diagnostic Evidence¶
| Evidence ID | Why Non-Diagnostic |
|---|---|
| SRC04-E01 | The Bowtie analysis supports both H1 and H3 equally — it documents harm (supporting H1) from automation bias mechanisms (supporting H3) |
Outcome¶
Hypothesis supported: H3 — Evidence exists primarily from automation bias (human over-reliance) in professional contexts, though the OpenAI incident shows system-side sycophancy can also cause harm.
Hypotheses eliminated: H2 — Abundant evidence eliminates the claim of no documented consequences.
Hypotheses inconclusive: H1 — Supported in the broad sense (harm is documented) but the specific mechanism in professional contexts is predominantly automation bias rather than system sycophancy.