When to Regulate AI

The report discusses the challenges and considerations in regulating AI, emphasizing the need for timely and flexible regulation to address ethical, social, and market concerns while balancing innovation and safety.

(Generated with the help of GPT-4)

The research method involves a comparative analysis of AI regulation approaches in different jurisdictions, historical context examination, and theoretical exploration of regulatory challenges like the Collingridge Dilemma. It critiques existing frameworks and suggests new regulatory principles for AI.

(Generated with the help of GPT-4)

The report explores the complexities of regulating artificial intelligence, drawing parallels with historical technological advancements. It highlights the need for regulation to address market failures, protect rights, and promote social policies. The European Union, United States, and China are examined as case studies, each with distinct approaches to AI regulation. The report discusses the Collingridge Dilemma, which highlights the difficulty of regulating technology early enough to prevent harm but late enough to understand its consequences. It suggests that regulation should focus on flexibility and reversibility. The report also critiques Asimov's three laws of robotics, emphasizing the need for new rules in AI regulation, particularly concerning human control and transparency. The demand for regulation is expected to grow as AI becomes more pervasive, with the nature of laws varying by jurisdiction.

(Generated with the help of GPT-4)

Categories: 2020s_time_horizon | 2025_time_horizon | china_geographic_scope | english_publication_language | european_union_geographic_scope | united_states_geographic_scope | ai_regulation | collingridge_dilemma | ethical_dilemmas | human_control | human_rights | jurisdictional_differences | market_failures | social_policies | technological_innovation | transparency