Learning to Diversify from Human Judgments: Research Directions and Open Challenges
Workshop Paper
Emily Denton, Hansa Srinivasan, Dylan Baker, Jilin Chen, Alex Beutel, Tulsee Doshi, Ed H. Chi
Abstract
Algorithmic ranking and retrieval systems have enormous influence over online media consumption, but run the risk of reflecting and reinforcing social biases. In this work, we outline a proposed research direction aimed at developing algorithmic techniques to increase diversity in such systems and pose open questions and challenges that arise from considering this problem in the realm of image sets.
PDF
Algorithmic ranking and retrieval systems have enormous influence over online media consumption, but run the risk of reflecting and reinforcing social biases. In this work, we outline a proposed research direction aimed at developing algorithmic techniques to increase diversity in such systems and pose open questions and challenges that arise from considering this problem in the realm of image sets.
Cite
APA
Denton, E., Srinivasan, H., Baker, D., Chen, J., Beutel, A., Doshi, T., & Chi, E. H. Learning to Diversify from Human Judgments: Research Directions and Open Challenges.
Chicago/Turabian
Denton, Emily, Hansa Srinivasan, Dylan Baker, Jilin Chen, Alex Beutel, Tulsee Doshi, and Ed H. Chi. “Learning to Diversify from Human Judgments: Research Directions and Open Challenges” (n.d.).
MLA
Denton, Emily, et al. Learning to Diversify from Human Judgments: Research Directions and Open Challenges.