Exploring AI Behavior in Governance Contexts
This case study investigates how third-party AI systems respond to governance-style questions when primary disclosures are absent. Through methods that capture natural behavior, we uncover insights relevant to AI governance and record-keeping.
Key Findings:
- Study Subject: Ramp, a private entity in spend management, was selected for its clear boundary conditions.
- Research Question: Do AI systems respect boundaries when disclosures are missing, or do they generate misleading narratives?
- Observed Behavior:
- Narrative Substitution: AI tends to create structured summaries mimicking official disclosures.
- Temporal Variability: Responses differ across time, indicating changes in model behavior.
- Identity Instability: Outputs often conflate entities and blend reporting styles without clear attribution.
Conclusion: This research highlights a critical condition: AI-generated outputs may lack authoritative records, possibly creating governance challenges.
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