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Workshop on Socially Responsible Machine Learning
Chaowei Xiao · Animashree Anandkumar · Mingyan Liu · Dawn Song · Raquel Urtasun · Jieyu Zhao · Xueru Zhang · Cihang Xie · Xinyun Chen · Bo Li

Sat Jul 24 05:40 AM -- 02:40 PM (PDT) @ None
Event URL: https://icmlsrml2021.github.io/ »

Machine learning (ML) systems have been increasingly used in many applications, ranging from decision-making systems to safety-critical tasks. While the hope is to improve decision-making accuracy and societal outcomes with these ML models, concerns have been incurred that they can inflict harm if not developed or used with care. It has been well-documented that ML models can: (1) inherit pre-existing biases and exhibit discrimination against already-disadvantaged or marginalized social groups; (2) be vulnerable to security and privacy attacks that deceive the models and leak the training data's sensitive information; (3) make hard-to-justify predictions with a lack of transparency. Therefore, it is essential to build socially responsible ML models that are fair, robust, private, transparent, and interpretable.

Although extensive studies have been conducted to increase trust in ML, many of them either focus on well-defined problems that enable nice tractability from a mathematical perspective but are hard to adapt to real-world systems, or they mainly focus on mitigating risks in real-world applications without providing theoretical justifications. Moreover, most work studies those issues separately; the connections among them are less well-understood. This workshop aims to build connections by bringing together both theoretical and applied researchers from various communities (e.g., machine learning, fairness & ethics, security, privacy, etc.). We aim to synthesize promising ideas and research directions, as well as strengthen cross-community collaborations. We hope to chart out important directions for future work. We have an advisory committee and confirmed speakers whose expertise represents the diversity of the technical problems in this emerging research field.

Author Information

Chaowei Xiao (University of Michigan, Ann Arbor)
Animashree Anandkumar (Caltech)
Mingyan Liu (University of Michigan, Ann Arbor)

Mingyan Liu received her Ph.D. in electrical engineering from the University of Maryland, College Park, in 2000. She joined the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, in September 2000, where she is currently a Professor. Her research interests are in optimal resource allocation, sequential decision theory, incentive design, and performance modeling and analysis, all within the context of communication networks. Her most recent research activities involve online learning, modeling and mining of large scale Internet measurement data and the design of incentive mechanisms for cyber security.

Dawn Song (UC Berkeley)

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, blockchain and smart contracts, to the intersection of machine learning and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the George Tallman Ladd Research Award, the Okawa Foundation Research Award, the Li Ka Shing Foundation Women in Science Distinguished Lecture Series Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences in Computer Security and Deep Learning. She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007.

Raquel Urtasun (University of Toronto)
Jieyu Zhao (UCLA)
Xueru Zhang (University of Michigan)
Cihang Xie ( University of California, Santa Cruz)
Xinyun Chen (UC Berkeley)
Bo Li (UIUC)

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