UVA Law and AI Course
This is all very preliminary! Nothing about this course is finalized, and everything is subject to change.
Meets jointly with LAW 7127: Artificial Intelligence and Machine Learning.
Expected Background:
- Formal Prerequisite: DMT2
- No previous background in law is expected, but students should be willing to read legal writings.
- Students are not required to have machine learning background, but should be comfortable enough with programming and math concepts to be able to go through a PyTorch tutorial on their own.
This course will explore connections between law and computing, with a focus on artificial intelligence and machine learning. We will consider ways computing systems can be designed and analyzed to satisfy laws and regulations, and how understanding of the law impacts how we design and reason about computing systems. Specific topics are likely to include:
- Liability: assessing responsibility when a computing system causes harm, and design computing systems for accountability.
- Explainability and Interpretability: how can we justify decisions made by machine learning systems and know when such systems can be trusted.
- Discrimination: how do anti-discrimination laws apply to computing systems, and how can we evaluate and improve the fairness of machine learning systems.
- Privacy: legal requirements for privacy and how they can be implemented with algorithms and software designs
- Copyright: how copyright applies to code, training data, and models, and technical measures for following copyright law.
This course meets jointly with a Law School class, and CS students will work closely with Law students throughout the semester, but will have some different assignments and will be graded separately. CS students will be expected to do assignments that involve programming as well as assignments that involve reading and writing on topics that connect law and computing.