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Date December 9, 2022 Location Virtual

While machine learning (ML) models have achieved great success in many applications, concerns have been raised about their potential security, privacy, fairness, transparency and ethics issues when applied to real-world applications. Irresponsibly applying machine learning to mission-critical and human-centric domains such as healthcare, education, and law can lead to serious misuse, inequity issues, negative economic and environmental impacts, and/or legal and ethical concerns.

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. Topics of this workshop include but are not limited to:

To attend the workshop, a Virtual Only Pass registration or any form of physical registration (either Conference or Workshop or both) at NeurIPS 2022 following this link is needed.

In-person Meet-up in NYC

For participants of the TSRML workshop in the evening after its conclusion, on December 9 around 8:00pm ET, we will have an in-person meet-up in NYC.

The tentative venue is Peter McManus Cafe (a casual Irish-style pub), located at 152 7th Ave, New York, NY 10011.

Please make a note of your interest using this form so that we can communicate logistics and update to an appropriately sized venue, if needed. You can also direct any questions to Melissa Hall via the last option in the form. Looking forward to meeting you who are interested and active in this space!

Thanks to Melissa Hall for making this happen!

Featured Speakers

Ordered alphabetically by last name.

Kamalika Chauduri Nika Haghtalab Been Kim Yi Ma
Kamalika Chauduri
University of California, San Diego
Nika Haghtalab
University of California, Berkeley
Been Kim
Google Brain
Yi Ma
University of California, Berkeley
Aleksander Mądry Marco Pavone Dorsa Sadigh Milind Tambe
Aleksander Mądry
Massachusetts Institute of Technology
Marco Pavone
Stanford University
Dorsa Sadigh
Stanford University
Milind Tambe
Harvard University


Huan Zhang Linyi Li Chaowei Xiao
Huan Zhang
Carnegie Mellon University
Linyi Li
University of Illinois Urbana-Champaign
Chaowei Xiao
Arizona State University & NVIDIA
Zico Kolter Anima Anandkumar Bo Li
Zico Kolter
Carnegie Mellon University
Anima Anandkumar
California Institute of Technology & NVIDIA
Bo Li
University of Illinois Urbana-Champaign

Diversity Statement

We organizers are committed to ensuring fairness and equality to all who attend, submit to, and review for the workshop. Our goal is to create a forum that fosters inclusion for all as we strive to build a supportive community for those who participate. All suggestions are encouraged and appreciated as we move forward. For examples, we have taken various steps to expand the diversity of the participants: The organizers and invited speakers have diverse backgrounds (e.g., gender, race, affiliations, seniority, and nationality). In particular, many are from underrepresented groups in STEM fields: the organizers include female scholars and the confirmed speakers include female scholars as well as researchers from underrepresented races. Additionally, the workshop is inclusive and covers a wide range of topics (e.g., fairness, transparency, interpretability, privacy, robustness, etc).