CDS DS-482/682
4 credit pts.
TBA
3 credit pts.
094288
2.5 credit pts.
TBA

Course // Responsible AI, Law, Ethics & Society

Spring ‘23

Past. Spring ‘20 | Spring ‘21 | Spring ‘22

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 four institutes: Boston University, Tel Aviv University, the Technion and Bocconi University with instructors and teaching assistants from each.

Audience. Multidisciplinary: LLB (Bachelor of Laws) & LLM (Master of Laws) students from Tel Aviv University, undergrad & grad Data Science / Engineering students from the Technion, undergrad & grad Data Science / Computer Science students from Boston University and LLM students from Bocconi University.

Schedule

Class Date Topics Verticals
1 March 21st
Tue
AI & Us Social Welfare
2 March 23rd
Thu
Liability & Robustness Autonomous Vehicles
3 March 28th
Tue
Discrimination & Fairness Labour Market
4 March 30st
Thu
Discrimination & Fairness - con't Healthcare
5 April 18th
Tue
Privacy Transportation
6 April 20th
Thu
Transparency & Explainability Finance
7 May 2nd
Tue
Integration: Content Moderation Social Media Platforms
8 May 9th
Tue
Project Presentations and Course Summary

Class Hours. The course comprises eight 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.

March 21st - May 9th 2023 | Tuesday, Thursday
9 am - 1 pm (Eastern Time Zone)
3 pm - 7 pm (Central European Time Zone)
4 pm - 8 pm (Israel Time Zone)

Boston University For Boston University Students Only.

This course is available for undergraduate students and graduate students (Master, Ph.D.) in Data Science and Computer Science.

A permission is required to register to this corse, please fill out this application form. Contact Shlomi Hod shlomi <AT> bu <DOT> edu or book a session during office hour if you have any questions.

In this semester, another course - similar, yet different- will also be taught at BU: CDS DS 457/657 Law for Algorithms (“LfA”). This guide will help to decide which course to take.

  1. All course meetings, except the last one (May 9th), are online via Zoom.
  2. For the last class, Tuesday, May 9th, 9 am - 1 pm, we will meet in-person at CDS 1646.
  3. In addition, we will have a mandatory in-person pre-course meeting at the beginning of the semester, Tuesday, January 24th, 9:30 am - 10:45 am at room CDS 1635.
  4. Due to the different dates of daylight saving timezone change the US and Israel, the first (March 21st) and the second (March 23rd) meetings will take place one hour later 10 am - 2 pm EST.

Staff.

Instructors

Prof. Avigdor Gal
Faculty of Industrial Engineering & Management
Technion

Prof. Niva Elkin-Koren
Faculty of Law
Tel Aviv University

Prof. Maria Lillà Montagnani
Department of Law
Bocconi University

Dr. Karni Chagal-Feferkorn
Assistant Professor
Department of Law
Academic Center Ramat-Gan

Shlomi Hod
Computer Science PhD student
Boston University

Teaching fellows

Adv. Hofit Wasserman Rozen
Business Manager at Microsoft R&D Israel
Law PhD candidate
Tel Aviv University

Shir Lissak
Data Science PhD student
Technion

Adv. Sivan Shachar, LLM
Teaching fellow
Tel Aviv University

Marie-Claire Najjar, LLM
Teaching fellow
Bocconi University

Megan Chen
Computer Science PhD student
Boston University

Learning Objectives.

  1. Multidisciplinary Dialogue
    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.

  2. Responsible AI Literacy
    By the end of the course, the students will …
    1. 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.
    2. possess introductory knowledge and skills to oversight and audit AI systems through their life cycle (design, development and deployment).
    3. be able to find and use resources to achieve all of the above.
  3. Professional Responsibility
    By the end of the course, the students will 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.

Teams. Every class is built around one central task that requires the integration of law and data science perspectives with ethical considerations. The tasks are performed in teams which will be formed before the start of the course. Teams are assigned by the course staff and are fixed for the duration of the course. Teams are designed to consist of mixed backgrounds and disciplines.

Participation. Multidisciplinary teamwork is an indispensable component in this course, so the active participation of all students is necessary for successful learning. Therefore, a student might miss at most one class, but only for a justified reason after confirming with the instructor of their respective institution at least 3 days in advance.

Pre-Class Assignments. There are few assignments to be done and submitted before some of the classes. The students will use the outcomes of these assignments in the class. The submissions are mandatory but not graded.

In-Class Assignments. In every class, all teams are required to submit a half-pager memo and a deck of a few slides at the end of each class. Each team will present twice during the course.

Final Project (TO BE UPDATED). In their final project, the teams will be asked to develop a new case-study that makes use of data sets and data science techniques to demonstrate a legal and ethical dilemma regarding Responsible AI, Law, Ethics and Society. Additional instructions here.

Evaluation. The assignments and the final project will be evaluated in terms of how well they reflect learning from readings and in-class discussion, with particular attention the integration of technical, legal, and ethical perspectives.

Grading Breakdown.
Team final project: 40%
Team in-class assignments (5% x 7 classes): 35%
Team presentations during class (7.5% × classes): 15%
Individual pre-class assignments (1% × 5 assignments): 5%
Individual participation: 5%