FAT* 2019 Translation Tutorial: Challenges of incorporating algorithmic fairness into industry practice
Despite mounting public pressure to design machine learning (ML) systems that treat all users fairly, industry practitioners face considerable challenges when translating research in algorithmic fairness into practice. This tutorial aims to reduce the gap between fairness research and industrial practice. We will provide an overview of organizational and technical challenges encountered in practice, covering stakeholder involvement, data gathering, resourcing, and prioritization of tradeoffs. The insights discussed are drawn based on direct practical experience as well as conversations, formal interviews, and surveys with industry practitioners. Attendees will gain useful insight into approaches taken by other practitioners, as well as potential pitfalls. Academic researchers and educators will gain insight into a number of understudied practical challenges that may present barriers to real-world research impact. Both researchers and practitioners will explore possibilities for mutually productive researcher-practitioner partnerships, and gain insight into challenges that may arise in forming and maintaining such partnerships.
- Date:
- Speakers:
- Henriette Cramer, Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna Wallach, Sravana Reddy, Jean Garcia-Gathright
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