Understanding AI Governance Risks: The Case for Evidentiary Breakdown
This article dives deep into the pervasive risks of AI-generated narratives, focusing on evidentiary failures rather than merely technical inaccuracies.
Key Points:
- Evidentiary Taxonomy: Organized by observable failures in AI outputs across various scenarios.
- Failure Modes include:
- Identity Conflation: Merging distinct entities, contaminating narratives.
- Fabricated Attribution: Citing non-existent documents in authoritative styles.
- Temporal Drift: Inconsistent narratives from identical prompts over time.
- Status Inflation: Converting speculative statements into asserted facts.
- Cross-Run Instability: Conflicting narratives emerging from identical inquiry sets.
The article clarifies that AI risks aren’t theoretical but observable failures that require governance strategies.
Conclusion: As enterprises face demands for evidence of AI outputs, defendability hinges on transparency.
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