A new area called “human-centered machine learning” (HCML) promises to balance technological possibilities with human needs and values. Put simply, HCML couples technical innovations in ML with social values like fairness, equality, and justice. The focus for HCML is broad; it includes fair and transparent algorithm design, human-in-the-loop decision-making, design for human-AI collaborations, and exploring the social impacts of ML.
However, there are no unifying guidelines on what “human-centered” means, nor how HCML research and practice should be conducted. People have worked to articulate a nascent set of values for HCML, but the concept is not clear and definitions come from many sides.
This article by Stevie Chancellor in Communications of the ACM draws on the interdisciplinary history of human-centered thinking, HCI, AI, and science and technology studies to propose best practices for HCML.
Stevie Chancellor is an assistant professor in the Department of Computer Science and Engineering at the University Of Minnesota, Twin Cities, MN, USA.