Machine learning (ML) tools are increasingly employed to inform and automate consequential decisions for humans, in areas such as criminal justice, medicine, employment, welfare programs, and beyond. ML has already established its tremendous potential to not only improve the accuracy and cost-efficiency of such decisions but also minimize the impact of certain human biases and prejudices. The technology, however, comes with significant challenges, risks, and potential harms. Examples include (but are not limited to) exacerbating discrimination against historically disadvantaged social groups, threatening democracy, and violating people's privacy. This workshop aims to bring together experts from a diverse set of backgrounds (ML, human-computer interaction, psychology, sociology, ethics, law, and beyond) to better understand the risks and burdens of big data technologies on society, and identify approaches and best practices to maximize the societal benefits of Machine Learning. The workshop takes a broad perspective on Human-centric ML and addresses a wide range of challenges from diverse, multi-disciplinary viewpoints. We strongly believe that for society to trust and accept the ML technology, we need to ensure the interpretability and fairness of data-driven decisions. We must have reliable mechanisms to guarantee the privacy and security of people's data. We should demand transparency, not just in terms of the disclosure of algorithms, but also in terms of how they are used and for what purposes. And last but not least, we need to have a modern legal framework to provide accountability and allow subjects to dispute and overturn algorithmic decisions when warranted. The workshop particularly encourages papers that take a multi-disciplinary approach to tackle the above challenges. One of the main goals of this workshop is to help the community understand where it stands after a few years of rapid development and identify promising research directions to pursue in the years to come. We, therefore, encourage authors to think carefully about the practical implications of their work, identify directions for future work, and discuss the challenges ahead. This workshop is part of the ELLIS “Human-centric Machine Learning” program.