Machine learning (ML) systems have been increasingly used in many applications, ranging from decision-making systems to safety-critical tasks. 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: (1) inherit pre-existing biases and exhibit discrimination against already-disadvantaged or marginalized social groups; (2) be vulnerable to security and privacy attacks that deceive the models and leak the training data's sensitive information; (3) make hard-to-justify predictions with a lack of transparency. Therefore, it is essential to build socially responsible ML models that are fair, robust, private, transparent, and interpretable.

Although extensive studies have been conducted to increase trust in ML, many of them either focus on well-defined problems that enable nice tractability from a mathematical perspective but are hard to adapt to real-world systems, or they mainly focus on mitigating risks in real-world applications without providing theoretical justifications. Moreover, most work studies those issues separately; the connections among them are less well-understood. 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.). We aim to synthesize promising ideas and research directions, as well as strengthen cross-community collaborations. We hope to chart out important directions for future work. We have an advisory committee and confirmed speakers whose expertise represents the diversity of the technical problems in this emerging research field.