Course // Responsible AI, Law, Ethics & Society
Spring 2021
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 campuses: 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.
Staff.
Prof. Avigdor Gal
Faculty of Industrial Engineering & Management
Technion
Prof. Niva Elkin-Koren
Faculty of Law
Tel Aviv University
Prof. Helen Nissenbaum
Information Science
Cornell Tech
Dr. Karni Chagal-Feferkorn
Law postdoctoral researcher
University of Ottawa
Shlomi Hod
CS PhD student
Boston University
TBA
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.
Schedule.
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.
| Class | Date | Topics | Verticals |
|---|---|---|---|
| 1 | May 27th Thu |
Liability & Robustness | Autonomous Vehicles |
| 2 | June 1st Tue |
Discrimination & Fairness | Labour Market |
| 3 | June 3rd Thu |
Transparency & Explainability | Finance (credit score) |
| 4 | June 7th Mon |
Privacy & Surveillance | Geolocation & Smart Cities |
| 5 | June 8th Tue |
TBA | TBA |
| 6 | June 10th Thu |
TBA | TBA |
| 7 | June 14th Mon |
TBA | TBA |
| 8 | June 15th Tue |
Integration: Content Moderation | Social Media Platforms |
| 9 | June 17th Thu |
Project Presentations and Summary | |
Learning Objectives. 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.
- … be aware of the impact of AI on individuals, groups, society and humanity, and proactively spot ethical issues and scan for unintended consequences and potential harms.
- … possess introductory knowledge and skills to oversight and audit AI systems through their life cycle (design, development and deployment).
- … be able to find and use resources to achieve all of the above.
- … take the first steps in shaping their responsibility as professionals, and be motivated to act upon it.
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.