Towards Practices for Human-Centered Machine Learning
It addresses the problem of unclear HCML practices for scholars and practitioners in computer science, though it is incremental in proposing guidelines rather than novel technical solutions.
The paper tackles the challenge of defining and implementing human-centered machine learning (HCML) by proposing five practices that integrate social, cultural, and ethical considerations with technical advances, bridging interdisciplinary perspectives.
"Human-centered machine learning" (HCML) is a term that describes machine learning that applies to human-focused problems. Although this idea is noteworthy and generates scholarly excitement, scholars and practitioners have struggled to clearly define and implement HCML in computer science. This article proposes practices for human-centered machine learning, an area where studying and designing for social, cultural, and ethical implications are just as important as technical advances in ML. These practices bridge between interdisciplinary perspectives of HCI, AI, and sociotechnical fields, as well as ongoing discourse on this new area. The five practices include ensuring HCML is the appropriate solution space for a problem; conceptualizing problem statements as position statements; moving beyond interaction models to define the human; legitimizing domain contributions; and anticipating sociotechnical failure. I conclude by suggesting how these practices might be implemented in research and practice.