Improving fairness in machine learning systems: What do industry practitioners need?
arXiv:1812.05239 · doi:10.1145/3290605.3300830
Abstract
The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.
To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019)
References in corpus (2)
Cited by in corpus (8)
- Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
- Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
- More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
- This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
- Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
- LiFT: A Scalable Framework for Measuring Fairness in ML Applications
- Mediating Community-AI Interaction through Situated Explanation: The Case of AI-Led Moderation
- Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination