R0051/2026-03-31/Q003/SRC05/E01¶
Generative AI deepens the epistemological gap — existing frameworks insufficient for AI-generated content.
URL: https://www.tandfonline.com/doi/full/10.1080/1369118X.2026.2630697
Extract¶
Cazzamatta (2026) argues that "defining 'fact' in the era of generative AI requires rethinking fundamental epistemological assumptions about evidence, verification, and what we consider truthful or factual knowledge." The paper introduces the concept of "emergent facts" — outputs that are "probabilistic, context-dependent, and epistemically opaque."
The paper analyzes three categories of facts (evidence-based, interpretative-based, and rule-based) and finds their "limitations when applied to AI-generated content." This deepens the documented gap — not only do formal evidence evaluation frameworks not exist for traditional fact-checking, but the emergence of AI-generated content creates additional epistemological challenges that existing informal methods cannot address.
Relevance to Hypotheses¶
| Hypothesis | Relationship | Strength |
|---|---|---|
| H1 | Contradicts | Gap is deepening, not being filled with formal solutions |
| H2 | Supports | Continued gap documentation, now extended to AI era |
| H3 | Contradicts | Active scholarly engagement with the gap as of 2026 |
Context¶
This is the most recent paper (2026) documenting the gap. Its focus on AI-generated "emergent facts" shows the gap is growing wider — formal evidence evaluation frameworks are needed more than ever but still do not exist.