Spring 2024 - Responsible AI, Law, Ethics & Society¶
3 credit pts.
2.5 credit pts.
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 two institutes: Tel Aviv University and the Technion.
Audience¶
Multidisciplinary: LLB (Bachelor of Laws) & LLM (Master of Laws) students from Tel Aviv University and undergrad & grad Data Science / Engineering students from the Technion.
Schedule¶
| Class | Date | Topics | Verticals |
|---|---|---|---|
| 1 | May 30th | AI & Us | Social Welfare |
| 2 | June 6th | Liability & Robustness | Autonomous Vehicles |
| NO CLASS | June 13th | Shavuot Holiday | |
| 3 | June 20th | Discrimination & Fairness | Labour Market |
| NO CLASS | June 27th | Students' Day | |
| 4 | July 4th | Deploying AI applications with foundation models & generative AI | Ecosystem |
| 5 | July 11th | Integration: Content Moderation | Social Media Platforms |
| 6 | July 18th | Project Presentations and Course Summary |
Class Hours¶
The course comprises six 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.
May 30th - July 18th 2024 | Thursday
4:30 pm - 8:30 pm
Staff¶
Instructors¶
Prof. Avigdor Gal
Faculty of Data & Decision Sciences
Technion
Prof. Niva Elkin-Koren
Faculty of Law
Tel Aviv University
Teaching fellows¶
Shir Lissak
Data Science PhD student
Technion
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 …
- 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.
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¶
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-sutdies, values and sectors that are not covered in the course.
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 (7% x 5 classes): 35%
- Team presentations during class (8% × 2 classes): 16%
- Individual pre-class assignments (1% × 4 assignments): 4%
- Individual participation: 5%