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Overview. The deployment of Artificial Intelligence systems in multiple domains of society raises fundamental challenges and concerns, such as accountability, liability, fairness, transparency and privacy. The dynamic nature of AI systems requires a new set of skills informed by ethics, law, and policy to be applied throughout the life cycle of such systems: design, development and deployment. It also involves ongoing collaboration among data scientists, computer scientists, lawyers and ethicists. Tackling these challenges calls for an interdisciplinary approach: deconstructing these issues by discipline and reconstructing with an integrated mindset, principles and practices between data science, ethics and law. This course aims to do so by bringing together students with diverse disciplinary backgrounds into teams that work together on joint tasks in an intensive series of in-class sessions. These sessions will include lectures, discussions, and group work.
This unique course also brings together students from three institutes: Cornell Tech, Tel Aviv University, and Technion with instructors and teaching assistants from each.
Poster about the course for the Workshop on Co-Development of Computer Science and Law 2020 at DIMACS, Rutgers University.
Prof. Avigdor Gal
Faculty of Industrial Engineering & Management
Prof. Niva Elkin-Koren
Faculty of Law
Tel Aviv University
Prof. Helen Nissenbaum
Dr. Karni Chagal-Feferkorn
Law postdoctoral researcher
University of Ottawa
Computer Science PhD student
Information Science PhD student
Data Science M.Sc. student
Audience. Multidisciplinary: approximately half the students are pursuing an LLM in Law from University of Tel-Aviv. The remaining half are Data Science & Engineering senior undergrad students from the Technion and Jacobs Technion-Cornell Dual Master students.
May 27-June 17 | Monday, Tuesday, Thursday
9:30 am - 1:30 pm (Eastern Time Zone)
4:30 pm - 8:30 pm (Israel Time Zone)
Structure. The course comprises nine meetings of four clock hours, in a workshop format. The topics explore some of the core issues in the landscape of Responsible AI, law, ethics and society.
|Liability & Robustness||Autonomous Vehicles|
|Discrimination & Fairness||Labour Market|
|Discrimination & Fairness - con't||Healthcare|
|Transparency & Explainability||Finance|
|Privacy & Surveillance||Geolocation|
|Values at Play||COVID-19|
|Integration: Content Moderation||Social Media Platforms|
|Accountability, AI Governance & Professional Responsibility||Smart Cities|
|Project Presentations and Summary|
Learning Objectives. By the end of the course, the students will…
By the end of the course, the students will be able to communicate with professionals from other disciplines, identify gaps in the meaning of terms and perspectives, and develop a shared language.
Format. The teaching is based on the signature pedagogy of each discipline; case-studies for Law and iterated and interactive research of data (e.g., with Jupyter Notebook) for Data Science. These two pedagogies are being used in every class, accessible to all of the students, and integrated together.
Tasks, Project and Evaluation. Every class is built around one central task that requires integration of law and data science perspectives. The tasks are performed in teams which will be formed before the start of the course. Each team consists of students from both disciplines.
Teams will present their work at least twice during the course, and all of the teams are required to submit a memo/presentation and a Jupyter Notebook at the end of each class. In their final project, the teams will be asked to develop a new case-study which makes use of data sets and data science techniques to demonstrate a legal\ethical dilemma regarding responsible AI, Law and Society.
The evaluation criteria will be focused on the integration of the law and data science perspectives.