Socially Responsible Machine Learning

Date: July 24, 2021

Location: Virtual Only (co-located with ICML 2021)

Abstract—Machine learning (ML) systems have been increasingly used in many applications, ranging from decision-making systems (e.g., automated resume screening and pretrial release tool) to safety-critical tasks (e.g., financial analytics and autonomous driving). 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:

For example, various commercial face recognition products were shown to have racial/gender bias. In domains such as financial analytics and autonomous vehicles, ML models could be easily misled by carefully-crafted small perturbation or even natural perturbation. Therefore, it is essential to build socially responsible Machine Learning models that are fair, robust, private, transparent, and interpretable.

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.). This workshop will focus on recent research and future directions for socially responsible machien learning problems in real-world machine learning systems. We aim to bring together experts from different communities in an attempt to highlight recent work in this area as well as to clarify the foundations of socially responsible machine learning.

Schedule

The tentative schedule is subject to change prior to the workshop.

Important Dates

Call For Papers

Submission deadline: June 10, 2021 Anywhere on Earth (AoE)

Notification sent to authors: July 10, 2021 Anywhere on Earth (AoE)

Submission server: https://cmt3.research.microsoft.com/ICMLSRML2021/

The workshop will include contributed papers. The workshop will be completely virtual. We will update the details later.

We invite submissions on any aspect of machine learning that relates to fairness, ethics, transparency, interpretability, security, and privacy. This includes, but is not limited to:

Submission Format: We welcome submissions up to 4 pages in ICML Proceedings format (double-blind), excluding references and appendix. Style files and an example paper are available. We allow an unlimited number of pages for references and supplementary material, but reviewers are not required to review the supplementary material. Unless indicated by the authors, we will provide PDFs of all accepted papers on https://icmlsrml2021.github.io. There will be no archival proceedings. We are using CMT3 to manage submissions.

Organizing Committee

Chaowei Xiao

Xueru Zhang

Cihang Xie

Xinyun Chen

Jieyu Zhao

Senior Organizing Committee

Anima Anandkumar

Bo Li

Mingyan Liu

Dawn Song

Raquel Urtasun