Executive - Fall 2023 - Responsible AI, Law, Ethics & Society¶
Overview¶
Schedule¶
| Class | Date | Topics | Verticals |
|---|---|---|---|
| 1 | January 2nd | AI & Us | Social Welfare |
| 2 | January 9th | Liability & Robustness | Autonomous Vehicles |
| 3 | January 16th | Discrimination & Fairness | Labour Market |
| 4 | January 23rd | Foundation Models & GenAI | Ecosystem |
| 5 | January 30th | Integration: Content Moderation | Social Media Platforms |
| 6 | February 6th | AI Governance & Project Presentations | - |
Class Hours¶
The course comprises seven 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 2nd - February 6th 2024 | Tuesday
4:30 pm - 8:30 pm (Israel Time Zone)
Staff¶
Prof. Niva Elkin-Koren
Faculty of Law
Tel Aviv University
Prof. Avigdor Gal
Faculty of Industrial Engineering & Management
Technion
Dr. Karni Chagal-Feferkorn
Tel Aviv University
Shlomi Hod
Computer Science PhD student
Boston University
Adv. Amit Ashkenazi
University of Haifa
Adv. Hofit Wasserman Rozen
Law PhD candidate
Tel Aviv 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 …
- 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.
- demonstrate 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.
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¶
Cross-disciplinary teamwork is an indispensable component in this course, so the active participation of all students is necessary for successful learning.
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 analysis of a Resopnsible AI tool. The analysis requires the integration of technological, legal and ethical perspectives.
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 (6% x 6 classes): 36%
- Team presentations during class (7% × 2 classes): 14%
- Individual pre-class assignments (1% × 5 assignments): 5%
- Individual participation: 5%