%0 Conference Paper %1 holstein2018improving %A Holstein, Kenneth %A Vaughan, Jennifer Wortman %A Daumé III, Hal %A Dudík, Miro %A Wallach, Hanna %D 2018 %K fairness %R 10.1145/3290605.3300830 %T Improving fairness in machine learning systems: What do industry practitioners need? %U http://arxiv.org/abs/1812.05239 %X 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.

@inproceedings{holstein2018improving, 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.}, added-at = {2019-12-25T02:08:35.000+0100}, author = {Holstein, Kenneth and Vaughan, Jennifer Wortman and Daumé III, Hal and Dudík, Miro and Wallach, Hanna}, biburl = {https://www.bibsonomy.org/bibtex/26a9430fc8432ec26f3296d270a932c13/kirk86}, description = {[1812.05239] Improving fairness in machine learning systems: What do industry practitioners need?}, doi = {10.1145/3290605.3300830}, interhash = {549a8d259a85f9a295db6c745dd69345}, intrahash = {6a9430fc8432ec26f3296d270a932c13}, keywords = {fairness}, note = {cite arxiv:1812.05239Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019)}, timestamp = {2019-12-25T02:08:35.000+0100}, title = {Improving fairness in machine learning systems: What do industry practitioners need?}, url = {http://arxiv.org/abs/1812.05239}, year = 2018 }