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Cleary Gottlieb Discusses Effective Board Oversight as AI Evolves

Feb 05, 2025 - clsbluesky.law.columbia.edu
In 2024, the deployment of generative AI rapidly expanded across various industries, enhancing productivity and return on investment. However, this growth also attracted attention from lawmakers and regulators, leading to 27 active generative AI litigation cases in the U.S., primarily involving copyright claims. While Congress has not yet engaged in AI legislation, the SEC and FTC have used existing laws for AI-related enforcement actions. Colorado became the first state to pass a comprehensive AI regulation law, while the EU implemented the EU AI Act, imposing stringent obligations on high-risk AI systems and providers of general-purpose AI models.

In the U.S., courts are addressing novel AI copyright issues, with key cases like _Thomson Reuters v. ROSS Intelligence_ and _Kadrey v. Meta Platforms_ focusing on whether AI training on copyrighted works constitutes fair use. State legislatures are also considering AI-related bills, particularly in employment and consumer protection contexts. Companies are advised to implement risk assessment frameworks and consider the implications of building versus buying AI solutions, while prioritizing cybersecurity and employee training to mitigate AI-related risks.

Key takeaways:

  • Novel AI copyright issues are being litigated, and courts will soon provide at least some answers to questions, such as the application of the fair use doctrine to various uses of AI.
  • Though federal AI legislation is not expected soon, state lawmakers have been relatively proactive with respect to AI, addressing issues such as employment and consumer protection. Likewise, the EU has adopted its own comprehensive AI regulatory scheme with broad jurisdiction.
  • Even without new AI-specific legislation, federal agencies have shown willingness to engage with AI-related issues and bring enforcement actions under existing law.
  • With the variety of legal risks in mind, a risk assessment framework designed around a combination of delegation and escalation lets boards and management streamline the AI vetting process and appropriately shifts board-level focus to high-risk AI tools.
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