This workshop will bring diverse researchers together to discuss and advance state-of-the-art identifying and addressing societal concerns in algorithms, machine learning and data analysis.
Background. 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.
Program now up! The program will span diverse topics: machine learning, algorithmic fairness, privacy, game theory, and connections to social sciences.
Plenary speakers:
- Steven Brams, NYU
- Mark Bun, Simons Institute
- Moon Duchin, Tufts University
- Shafi Goldwasser, Weizmann and UC Berkeley
- Joe Halpern, Cornell
- Moritz Hardt, UC Berkeley
- Yehuda Lindell, Bar Ilan University
- Rafael Pass, Cornell University and Cornell Tech
- Nati Srebro, TTI Chicago