Human-Centric Artificial Intelligence is the notion of developing and using AI systems to help enhance, augment, and improve the quality of human life. Naturally, this paradigm involves two major components: human-centered computing and representation learning and responsible AI in human-centric applications. The first component revolves around tasks such as user authentication, activity recognition, pose estimation, affective computing, health analytics, and others, which often rely on modeling data with specific spatiotemporal properties, for instance human activity images/videos, audio signals, sensor-based time-series (e.g., PPG, ECG, EEG, IMU, clinical/medical data), and more. In recent years, learning effective representations for computer vision and natural language has revolutionized the effectiveness of solutions in these domains. Nonetheless, other data modalities, especially human-centric ones, have been largely under-served in terms of research and development. For these under-served domains, the general attitude has been to take advances from the ‘vision’ or ‘NLP’ communities and adapt them where possible. We argue, however, that a more original and stand-alone perspective on human-centric data can be highly beneficial and can lead to new and exciting advancements in the area. While the first component of this workshop mostly covers interpretation of people by AI, the second key component of the workshop is centered around interpretation of AI by people. This means aiding humans to investigate AI systems to facilitate responsible development, prioritizing concepts such as explainability, fairness, robustness, and security. We argue that identifying potential failure points and devising actionable directions for improvement is imperative for responsible AI and can benefit from translating model complexities into a language that humans can interpret and act on. Hence, this workshop also aims to cover recent advances in the area of responsible AI in human-centric applications. In the R2HCAI workshop, we aim to bring together researchers broadly interested in Representation Learning for Responsible Human-Centric AI to discuss recent and novel findings in the intersection of these communities.