Overview

With the increasing deployment of machine learning in diverse applications affecting our daily lives, ethical and legal implications are rising to the forefront. Governments worldwide have responded by implementing regulatory policies to safeguard algorithmic decisions and data usage practices. However, there appears to be a considerable gap between current machine learning research and these regulatory policies. Translating these policies into algorithmic implementations is highly non-trivial, and there may be inherent tensions between different regulatory principles.

This workshop aims to provide a platform for: i) discussing various algorithmic, technical, and policy challenges that arise when operationalizing various guidelines outlined in existing regulatory frameworks, and ii) finding solutions to mitigate and address these challenges.

Please check out our Call for Papers.

Important Dates:

Organizers

If you have any questions, please contact us via the following email: regml-2024@googlegroups.com.

Core Organizing Team

Jiaqi Ma

Jiaqi Ma

University of Illinois
Urbana-Champaign

Chirag Agarwal

Chirag Agarwal

University of Virginia
 

Sarah Tan

Sarah Tan

Salesforce
Cornell University

Hima Lakkaraju

Hima Lakkaraju

Harvard University
 

Student Organizers

Usha Bhalla

Usha Bhalla

Harvard University
 

Zana Bucinca

Zana Bucinca

Harvard University
 

Junwei Deng

Junwei Deng

University of Illinois
Urbana-Champaign

Alex Oesterling

Alex Oesterling

Harvard University
 

Shichang Zhang

Shichang Zhang

Harvard University