R0048/2026-04-01/Q003/H2¶
Statement¶
Training materials address hallucination but characterize it as random fabrication — the AI "makes things up" or produces "inaccurate outputs" — without connecting it to sycophancy or recognizing that some hallucinations are specifically generated to match user expectations.
Status¶
Current: Supported
Supporting Evidence¶
| Evidence | Summary |
|---|---|
| SRC03-E01 | DOL names "hallucinations and accuracy limits" — framed as accuracy problems, not behavioral tendencies |
| SRC01-E01 | IAPP characterizes hallucination as fundamental but frames it as a technical/governance problem, not connected to user influence |
| SRC02-E01 | Technical blog explicitly describes the connection, but this is AI safety research, not training content |
Contradicting Evidence¶
| Evidence | Summary |
|---|---|
| SRC04-E01 | Fortune/Science coverage shows the connection is known in research — contradicts the characterization as random (for the research community, not for training) |
Reasoning¶
The DOL framework's framing of "hallucinations and accuracy limits" as a subtopic of "Understand AI Principles" is the most specific hallucination training guidance found. It names the problem but does not characterize how hallucination works or why it occurs. The IAPP governance analysis comes closest to characterizing hallucination as fundamental (not random), but even this sophisticated source does not connect it to sycophancy. Training materials universally treat hallucination as an output accuracy problem requiring verification, not as a behavioral tendency influenced by user expectations.
Relationship to Other Hypotheses¶
H2 is the dominant finding. Hallucination exists in training as a named problem but is understood as random error requiring output verification. The deeper understanding (fundamental property, spectrum including sycophancy) remains in AI safety research only.