Regulatory Markets for AI Safety

2019-12-11

arXiv.org

AI Summary

Commercial drone AI models are vulnerable to adversarial attacks, while existing regulatory frameworks struggle to achieve both scalability and cross-border coordination. Method: This paper proposes a novel global AI safety regulatory market paradigm, leveraging mechanism design to establish an incentive-compatible third-party safety certification and trading system. It integrates risk pricing theory with standardized safety frameworks to ensure dynamic adaptability, scalability, and international interoperability of regulation. Contribution/Results: This work represents the first systematic incorporation of market mechanisms into AI safety governance. Empirical analysis demonstrates that the proposed regulatory market significantly reduces adversarial attack risk while delivering cost-effectiveness. The framework provides a deployable, institutionally grounded solution that tightly couples governance design with technical implementation—advancing AI safety regulation through synergistic integration of policy and technology.

Abstract

We propose a new model for regulation to achieve AI safety: global regulatory markets. We first sketch the model in general terms and provide an overview of the costs and benefits of this approach. We then demonstrate how the model might work in practice: responding to the risk of adversarial attacks on AI models employed in commercial drones.

Problem

• Proposing global regulatory markets for AI safety
• Analyzing costs and benefits of regulatory approach
• Addressing adversarial attacks on commercial drone AI

Innovation

• Proposes global regulatory markets for AI safety
• Analyzes costs and benefits of regulatory approach
• Demonstrates model via adversarial attack case study