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About Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
This document summarizes research that investigated challenges faced by commercial machine learning product teams in developing fair machine learning systems. Through interviews with 35 individuals across 25 machine learning product teams from 10 major companies and an anonymous survey of 267 practitioners, the research identified several key findings. Practitioners reported that ensuring fair data collection was more important than algorithmic debiasing, and they struggled to apply existing auditing and debiasing methods. Practitioners also emphasized the need for holistic, system-level fairness auditing and consideration of human biases throughout the development process. The research aims to inform the design of fair machine learning tools based on real-world industry needs.
Detailed Information
| Author: | ['Chris'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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