Improving fairness in machine learning systems: What do industry practitioners need?
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningFairnessSimilar Papers 제목 키워드 기반
Fairness Practices in Industry: A Case Study in Machine Learning Teams Building Recommender Systems
The rapid proliferation of recommender systems necessitates robust fairness practices to address inherent biases. Assessing fairness, though, is challenging due to constantly evolving metrics and best practices. This pap…
FairnessRecommendation SystemsExploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how …
BIG-bench Machine LearningFairnessThe What-If Tool: Interactive Probing of Machine Learning Models
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source applicati…
BIG-bench Machine LearningFairnessInvestigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice
An emerging body of research indicates that ineffective cross-functional collaboration -- the interdisciplinary work done by industry practitioners across roles -- represents a major barrier to addressing issues of fairn…
Fairness"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness
As calls for fair and unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness in industry. However, these practitioners often do not have access to the demographic dat…
Fairness