This cross-cutting and interdisciplinary graduate course, taught jointly between the School of Law and the Faculty of Computing & Data Sciences, investigates the role that algorithms and automated decision-making systems play in law and society. The course connects legal and computational concepts of transparency, trustworthiness, privacy, secrecy, bias, discrimination, and fairness through a series of case studies that present recent applications of technology to legal and regulatory situations and explore the challenges in regulating algorithms and using algorithms in legal systems.
Legal concepts explored will include evidence and expert witnesses, anti-discrimination law concepts of disparate impact and disparate treatment, regulation of civic data gathering activities like the census, sectoral information privacy regimes, and public access and transparency laws. Computational concepts explored will include basics of modeling and automation, software design and development, artificial intelligence and machine learning, and privacy-enhancing technologies.
For more, please see the Syllabus, Reading List, and Assignments.