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