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Spring 2022 - Responsible AI, Law, Ethics & Society

Boston University Logo
CDS DS-482/682
4 credit pts.
Tel Aviv University Logo
3 credit pts.
Technion Logo
3 credit pts.
Bocconi University Logo
3 credit pts.


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.


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


Class Date Topics Verticals
1 March 22nd
AI & Us Social Welfare
2 March 24th
Liability & Robustness Autonomous Vehicles
3 March 29th
Discrimination & Fairness Labour Market
4 March 31st
Discrimination & Fairness - con't Healthcare
5 April 5th
Privacy Transportation
6 April 26th
Transparency & Explainability Finance
7 April 28th
Integration: Content Moderation Social Media Platforms
8 May 10th
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 22 - May 10 2022 | 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 Logo 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 the pre-course office hours 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. We will have a mandatory pre-course meeting on the first day of the semester, Thursday, January 20 2022 at 11:00 am - 12:15 pm.
  2. We will meet in-person for all classes at MCS B51. You must bring your laptops.
  3. For the pre-course meeting, we will meet at at MCS 141.
  4. Due to the different dates of daylight saving timezone change the US and Israel, the first (March 22th) and the second (March 24th) meetings will take place one hour later 10 am - 2 pm EST.



Prof. Avigdor Gal
Faculty of Industrial Engineering & Management

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

Prof. Maria Lillà Montagnani
Department of Law
Bocconi University

Dr. Karni Chagal-Feferkorn
Law postdoctoral researcher
University of Ottawa

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

Ari Karchmer
Computer Science PhD student
Boston University

Shir Lissak
Data Science M.Sc. student

Adv. Sivan Shachar, LLM
Teaching fellow
Tel Aviv University

Marie-Claire Najjar, LLM
Teaching fellow
Bocconi University

Eldar Abraham
Data Science M.Sc. student

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.


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.


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.


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

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.


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. We will publish a rubric soon.

Grading Breakdown

  • Final project: 40%
  • In-class assignments (slide deck x 7 class): 40%
  • Presentation during class (x2 for each team): 20%