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.
A fall program at Weizmann, running from October 1st through December 31st, will bring diverse researchers together to discuss and advance the state-of-the-art on identifying and addressing societal concerns in algorithms, machine learning and data analysis. This course, which will follow the program, is meant for highly-motivated students who may be interested in pursuing research on these topics.
Improtant Notice: Students are expected to attend the (full day) tutorial and lecture kick-off events on October 3,4,10,11, the weekly meeting and seminar on Thursdays, from October 18th through December 27th, and the workshop, December 16-20. Note that these activities begin before the official start date of the semester.
Before each seminar, students will review background for that lecture. Each student will be expected to present background information for at least one lecture in the seminar series.