References

Improving fairness in machine learning systems; what do industry practitioners need?

Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudik, & Hanna Wallach (2019)

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1-16.

DOI: https://doi.org/10.1145/3290605.3300830

Abstract. Holstein et al.'s industry-practitioner survey on fairness tooling — finding a substantial gap between academic-fairness research and what ML engineers actually need to deploy. Influential in fairness-tooling-UX literature.

Tags: ai-usability fairness industry

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