SCADA 2021: Societal Concerns in Algorithms and Data Analysis

Spring 2021

Weizmann Institute of Science

SCADA 2021 Societal Concerns in Algorithms and Data Analysis

Spring Semester 2021

Weizmann Institute of Science

Overview. Machine learning and data analysis have enjoyed tremendous progress in a broad range of domains. These advances hold the promise of great benefits to individuals, organizations, and society as a whole. This progress, however, raises (and is impeded by) a host of concerns. Research has shown that existing machine learning methods can be vulnerable to adversarial attacks, might introduce biases that lead to discrimination and can leak information in a manner that compromises individuals’ privacy. Addressing these vulnerabilities and shortcomings can help society to harness the full power and potential of advances in data science and machine learning.

This course will be devoted to the presentation and discussion of papers that deal with identifying and addressing societal concerns in algorithms, machine learning and data analysis.

NEW: List of student presentations, slides and papers

Before each seminar, students will review background for that lecture. Each student will be expected to present at least one topic in the seminar series.

A website for a past program on these issues can be consulted for further background.

Lecturers: Moni Naor and Guy Rothblum

Time: Wednesdays 14:15-16:00
Zoom Link

Staying in the loop. If you are interested in participating in any or all of these activities, you can join the Google group (this will also give you access to the calendar)

For the Shapley Value see Chapter 12 in Karlin-Peres and any ``standard text" on Game Theory.

Sergiu Hart, SHAPLEY VALUE, The New Palgrave: Dictionary of Economics

See Chapter 13 in Karlin-Peres and Chapter 9.2 in Algorithmic Game Theory

Lecture 5: Causality, Gal Yona. Presentation slides.

Student presentations, papers and slides

Reading Material