As machine learning (ML) is deployed pervasively, there is an increasing demand for ML systems to behave reliably when the input to the system has changed. Much work has emerged regarding artificial and natural changes to data, with a growing interest towards studying robustness and reliability of ML systems in the presence of real-world changes. This shift towards more realistic considerations raises both old and new fundamental questions for machine learning:
1. Can we bring principled research in robustness closer to real-world effects?
2. How can we demonstrate the reliability of ML systems in real-world deployments?
3. What are the unique societal and legal challenges facing robustness for deployed ML systems?
Consequently, the goal of this workshop is to bring together research in robust machine learning with the demands and reliability constraints of real-world processes and systems, with a focus on the practical, theoretical, and societal challenges in bringing these approaches to real world-scenarios. We highlight emerging directions, paradigms, and applications which include 1. Characterizing real-world changes for robustness; 2. Reliability of real-world systems; 3. Societal and legal considerations.