Machine learning systems are increasingly employed to inform and automate consequential decisions for humans in areas such as criminal justice, medicine, employment, and welfare programs. However, this comes with significant challenges, risks, and potential harm. These
concerns have led to a surge of interest in the development of Human-Centric Machine Learning.
This young field seeks to minimize the potential harm, risks, and burdens of machine learning systems on the public, while at the same time maximizing their societal benefits. Our workshop will focus on addressing two themes that have been the subject of heated debate in recent
months: (i) the differential treatment by algorithms of historically under-served and disadvantaged communities and (ii) the development of machine learning systems to help humans peform better rather than be replaced.
The workshop will bring together leading experts at the forefront of our two themes, from diverse backgrounds. One of our workshop goals is to reflect on the legitimacy of the status quo, which often takes the objective of the algorithm's owner as a normative goal and sometimes assumes the algorithm has full agency (i.e., the algorithm is designed for full automation).