Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. To what extent do the representations formed by these diverse systems agree? Can diverging representations still lead to the same behaviors? And how can systems modify their representations to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most active research areas in contemporary cognitive science, neuroscience, and machine learning. However, despite this shared goal, the machine learning, neuroscience, and cognitive science communities that study alignment currently lack a shared framework for conveying insights across methodologies and disciplines. This workshop aims to bridge this gap by defining, evaluating, and understanding the implications of representational alignment among biological & artificial systems.