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Spring 2024 - Responsible AI, Law, Ethics & Society

Boston University Logo
CDS DS-482/682
4 credit pts.
University of California Berkeley Logo
Legal Studies 190
TBA 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 two institutes: University of California, Berkeley and Boston University with staff from each.


Cross-disciplinary: undergrad Legal Studies students from Univerisy of California, Berkely and undergrad & grad Data Science / Computer Science students from Boston University.


Class Date Topics Verticals
0 January 22th Introduction to Responsible AI -
1 January 29th AI & Us Social Welfare
2 February 5th Liability & Robustness Autonomous Vehicles
3 February 12th Discrimination & Fairness Labour Market
4 February 26th Discrimination & Fairness - con't Healthcare
5 March 4th Privacy Transportation

Boston University Logo Only
March 11th Guest lecture -
6 March 18th Deploying AI applications with foundation models & generative AI Ecosystem
7 April 1st Transparency & Explainability Finance
8 April 8th Integration: Content Moderation Social Media Platforms

Boston University Logo Only
April 15th Guest lecture -
9 April 22th AI Governance -
10 April 29th Project Presentations and Course Summary -

Class Hours

The course comprises 13 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.

January 22th - May 6th 2023 | Monday
12:10 pm - 4:10 pm (Pacific Time Zone)
3:10 pm - 7:10 pm (Eastern 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 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.

All course meetings are online via Zoom.



Shlomi Hod
Computer Science Department
Boston University

Talia Schwartz
Faculty of Law
University of California, Berkeley
University of Haifa

Teaching Fellows

Megan Chen
Computer Science Department
Boston University

Learning Objectives

1. Cross-disciplinary 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. demonstrate 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.


Cross-disciplinary 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

The teams will conduct an algorithmic audit of an AI system within a concrete context. The audit requires the integration of technological, legal and ethical perspectives on novel case-studies, values and sectors that are not covered in the course.


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 (60%)

  • Final project (40%)
  • In-class assignments (10%)
  • In-class presentations (10%)

Individual (40%)

  • Attendance (10%)
  • Participation (10%)
  • Pre-class assignments (10%)
  • Writing assignment at class 4 (10%)