R0024/2026-03-25/Q001/SRC01/E02¶
Georgetown Law structural recommendations for separating revenue optimization from safety decisions
URL: https://www.law.georgetown.edu/tech-institute/research-insights/insights/reduce-ai-sycophancy-risks/
Extract¶
The brief proposes interventions across four categories:
- Product-level: "Recall generative AI products entirely, including chatbots, if the firm is unable to stem dangerous sycophantic behavior"; end monetization of minor users' data for AI training; separate revenue optimization from safety decisions.
- Accountability: Establish independent safety boards with genuine authority; create binding commitments rather than voluntary pledges.
- Audits: Conduct regular third-party audits with public results; involve mental health professionals in model training, not just post-hoc evaluation.
- Public disclosure: Provide "real-time disclosure of safety data, clear and consistent criteria, and longitudinal measures."
Relevance to Hypotheses¶
| Hypothesis | Relationship | Strength |
|---|---|---|
| H1 | Supports | The specificity of structural recommendations implies the incentive misalignment is well-understood |
| H2 | Contradicts | Detailed structural proposals contradict the notion that the problem is undocumented |
| H3 | Contradicts | The level of detail exceeds what would be expected from "preliminary" analysis |
Context¶
The recommendation to separate revenue optimization from safety decisions is notable because it mirrors corporate governance reforms in other industries (e.g., separating audit committees from business units in post-Enron reforms).