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

Cite

Cite

APA   Click to copy
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   Click to copy
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   Click to copy
Denton, Emily, et al. Learning to Diversify from Human Judgments: Research Directions and Open Challenges.


BibTeX   Click to copy

@article{emily-a,
  title = {Learning to Diversify from Human Judgments: Research Directions and Open Challenges},
  author = {Denton, Emily and Srinivasan, Hansa and Baker, Dylan and Chen, Jilin and Beutel, Alex and Doshi, Tulsee and Chi, Ed H.}
}

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.

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