Frontier AI Regulation¶
Anderljung, M., Barnhart, J., & Korinek, A. (2023). Frontier AI Regulation: Managing Emerging Risks to Public Safety.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Collingridge Dilemma
- Organizational design — founding-stage choices about equity, charter, and norms are easy to set and hard to revise once the organization has scaled. Medicine — population-scale introduction of preventive therapies trades informationally-cheap early abandonment against well-targeted but un-reversible late exposure. AI governance — the dominant frame for frontier-model deployment, sharpened because capabilities scale faster than evaluation, deployed systems shape downstream economics, and weight release is irreversible.
This sourceArgues frontier-AI capabilities can scale faster than evaluation and that deployment is hard to reverse, motivating staged deployment, pre-deployment evaluations, and eval-and-respond loops — the Collingridge/pacing problem applied to AI governance.
- Organizational design — founding-stage choices about equity, charter, and norms are easy to set and hard to revise once the organization has scaled. Medicine — population-scale introduction of preventive therapies trades informationally-cheap early abandonment against well-targeted but un-reversible late exposure. AI governance — the dominant frame for frontier-model deployment, sharpened because capabilities scale faster than evaluation, deployed systems shape downstream economics, and weight release is irreversible.
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Registry ID ref:c08260653757 · see in the full table