Base AI policy on evidence, not existential angst

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Base AI policy on evidence, not existential angst

The discourse surrounding AI policy has intensified, especially following California's recent legislative actions. Despite the veto of SB 1047, numerous AI-related bills are still under consideration, leaving uncertainty about federal regulatory approaches. The challenge lies in establishing a reasonable policy amidst extreme viewpoints and widespread confusion about AI's implications.

A focus on marginal risk is proposed as a sensible regulatory strategy, emphasizing the need to address new risks introduced by AI technologies. This approach aims to avoid ineffective regulations that could stifle innovation and economic growth. By grounding AI policy in evidence and real-world impacts, the potential benefits of AI can be maximized while addressing legitimate concerns.

• California's SB 1047 veto highlights ongoing AI regulatory challenges.

• Focusing on marginal risk can lead to more effective AI regulations.

Key AI Terms Mentioned in this Article

Marginal Risk

Marginal risk refers to new risks introduced by emerging technologies that require updated policies.

AI Regulation

AI regulation involves creating policies to manage the risks and impacts of artificial intelligence.

Evidence-Based Policy

Evidence-based policy relies on thorough research and real-world data to inform regulatory decisions regarding AI.

Companies Mentioned in this Article

OpenAI

OpenAI is a leading AI research organization known for developing advanced AI models like GPT-2 and GPT-3.

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