Towards Practices for Human-Centered Machine Learning
"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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
01-03-2022
|
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | "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. |
|---|---|
| DOI: | 10.48550/arxiv.2203.00432 |