Towards Practices for Human-Centered Machine Learning

TL;DR

This paper proposes five interdisciplinary practices to guide human-centered machine learning, emphasizing social, cultural, and ethical considerations alongside technical development.

Contribution

It introduces a set of five practical guidelines for implementing human-centered ML, integrating perspectives from HCI, AI, and sociotechnical fields.

Findings

Five core practices for HCML are identified.

Guidelines for integrating social and ethical considerations.

Strategies for implementing HCML in research and practice.

Abstract

"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.