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Trustworthy and Socially Responsible Machine Learning

Huan Zhang · Linyi Li · Chaowei Xiao · J. Zico Kolter · Anima Anandkumar · Bo Li


Fri 9 Dec, 6:45 a.m. PST

To address these negative societal impacts of ML, researchers have looked into different principles and constraints to ensure trustworthy and socially responsible machine learning systems. This workshop makes the first attempt towards bridging the gap between security, privacy, fairness, ethics, game theory, and machine learning communities and aims to discuss the principles and experiences of developing trustworthy and socially responsible machine learning systems. The workshop also focuses on how future researchers and practitioners should prepare themselves for reducing the risks of unintended behaviors of sophisticated ML models.

This workshop aims to bring together researchers interested in the emerging and interdisciplinary field of trustworthy and socially responsible machine learning from a broad range of disciplines with different perspectives to this problem. We attempt to highlight recent related work from different communities, clarify the foundations of trustworthy machine learning, and chart out important directions for future work and cross-community collaborations.

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Timezone: America/Los_Angeles