Algorithm Design and Law: Potential and Challenges
summary
Asks whether algorithm design can encode legal doctrines and gives concrete examples: a formal model called Responsibility-Sensitive Safety (RSS) translates duty of care into parameters (response time, max braking/accel) and Mobileye founders test it on real cars with empirical success; papers show independent pricing algorithms learn to collude through repeated learning without communication, exposing a gap in antitrust law; work on personalized law explores implementing tailored legal rules as algorithms. Highlights tool gaps, worst-case vs. open-ended norms, and several impossibility results.
AI-written summary · about
chapters (13)
- 0:00Introduction and background
- 1:20Bridging CS and law
- 4:47Societal impact of algorithms
- 8:09Opportunities for collaboration
- 12:50Current interdisciplinary signs
- 16:12Challenges in interdisciplinary work
- 21:16Worst-case approach in algorithms
- 24:34Legal doctrines vs algorithms
- 28:44Institutional structures
- 30:47Case studies of legal formalization
- 35:41Model for safe self-driving cars
- 42:28Future research and implications
- 50:22Panel commentary and closing
related talks
06mThe Law Bites Before the Code Does: Designing Vehicle...
28mDemystifying the emerging legal issues of AI
31mEthics of algorithms - from theoretical discussion to practical application
17mAI Law & Ethics: developing responsible AI systems
41mAI and the Law
34mLegal Barriers To Interpretable Machine Learning
46mAlgorithmic Transparency and Accountability
35mBuilding Inclusive Machine Learning Models