AI Alignment: Principal-Agent Models and Challenges

Explore the multifaceted challenges of aligning artificial intelligence with human values and objectives, drawing parallels and distinctions with established economic theories like the principal-agent problem. Weigh out the difficulties in specifying complete and accurate goals for AI, leading to potential misalignment where AI agents optimize for incomplete or misinterpreted proxies. Investigate methods for mitigating these risks, including conservative optimization, dynamic incentive protocols, and incorporating uncertainty about human preferences into AI decision-making. Examine how AI, as an increasingly capable agent, can create and understand information in ways that differ fundamentally from humans, raising critical questions about trust, control, and the future of human-AI coexistence. Learn about empirical studies using large language models that are used to investigate the emergence of principal-agent conflicts in AI and the implications for alignment strategies.

Listen to the Deep Dive audio podcast discussion on the subject matter:

Principal-Agent Models and Challenges 

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References: 

• Dae-Hyun Yoo, Caterina Giannetti "A Principal-Agent Model for Ethical AI: Optimal Contracts and Incentives for Ethical Alignment"
• Reddit thread, found on the "transhumanism forum", engages in a discussion sparked by the question of whether AI alignment is a non-problem or an unsolvable one. 
• Article from Lawfare discusses the emerging challenges of governing AI agents 
• "Challenges of AI systems: A new kind of principal–agent problem," featured on the website of the Institute of Mathematical Statistic 
• "Incentive Compatibility for AI Alignment in Sociotechnical Systems: Positions and Prospects" by Zhaowei Zhang, Fengshuo Bai Mingzhi, Wang Haoyang, Ye Chengdong, Ma Yaodong Yang 
• "Principal-Agent Problems" – subscriber discussion written by Jim Babcocklast updated 9th Nov 2020
• “principal-agent alignment problem within artificial intelligence” by Dylan Hadfield-Menell - Electrical Engineering and Computer Sciences University of California, Berkeley - Technical Report No. UCB/EECS-2021-207
• “What can the principal-agent literature tell us about AI risk?” by apc8th Feb 2020AI Alignment Forum
• “How the principal-agent literature relates to AI risk” by Rohin Shah 27th Feb 2020 Alignment NewsletterAI Alignment Forum
• “OF MODELS AND TIN MEN- A BEHAVIOURAL ECONOMICS STUDY OF PRINCIPAL-AGENT PROBLEMS IN AI ALIGNMENT USING LARGE-LANGUAGE MODELS” by Steve Phelps and Rebecca Ranson
• Yuval Noah Harari: ‘How Do We Share the Planet With This New Superintelligence?’ - Wired magazine, April 1 2025