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

Course // Responsible AI, Law, Ethics & Society

Spring ‘22

Past. Spring ‘20 | Spring ‘21

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: Boston University, Tel Aviv University, and Technion with instructors and teaching assistants from each.

For Interested Instructors. Fill out this form to receive updates about the release of the course materials. You can read more about the course in these poster from the Workshop on Co-Development of Computer Science and Law 2020 at DIMACS, Rutgers University.

Staff.

Instructors

Prof. Avigdor Gal
Faculty of Industrial Engineering & Management
Technion

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

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

Shlomi Hod
Computer Science PhD student
Boston University

Teaching fellows

TBA

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

Schedule. The course comprises nine virtual 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)
4 pm - 8 pm (Israel Time Zone)

IMPORTANT: 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 for the BU students.

Class Date Topics (TENTATIVE) Verticals
1 March 22th
Tue
AI & Us
2 March 24th
Thu
Liability & Robustness Autonomous Vehicles
3 March 29th
Tue
Discrimination & Fairness Labour Market
4 March 31st
Thu
Discrimination & Fairness
RAI Framework for the Data Science Workflow
Healthcare
5 April 5th
Tue
Transparency & Explainability Finance
6 April 26th
Tue
Privacy Transportation, COVID-19
7 April 28th
Thu
Integration: Content Moderation Social Media Platforms
8 May 10th
Thu
Project Presentations, Accountability, Professional Responsibility and Course Summary

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
    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. 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. 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%