Workshop Paper
Jessie Smith, Nasim Sonboli, Casey Fiesler, Robin Burke
Cite
Cite
APA
Click to copy
Smith, J., Sonboli, N., Fiesler, C., & Burke, R. Exploring User Opinions of Fairness in Recommender Systems.
Chicago/Turabian
Click to copy
Smith, Jessie, Nasim Sonboli, Casey Fiesler, and Robin Burke. “Exploring User Opinions of Fairness in Recommender Systems” (n.d.).
MLA
Click to copy
Smith, Jessie, et al. Exploring User Opinions of Fairness in Recommender Systems.
BibTeX Click to copy
@article{jessie-a,
title = {Exploring User Opinions of Fairness in Recommender Systems},
author = {Smith, Jessie and Sonboli, Nasim and Fiesler, Casey and Burke, Robin}
}
Abstract
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to trade offs between optimizing accuracy for users and fairness to providers. But what is fair in the context of recommendation--particularly when there are multiple stakeholders? In an initial exploration of this problem, we ask users what their ideas of fair treatment in recommendation might be, and why. We analyze what might cause discrepancies or changes between user's opinions towards fairness to eventually help inform the design of fairer and more transparent recommendation algorithms.
PDF
Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to trade offs between optimizing accuracy for users and fairness to providers. But what is fair in the context of recommendation--particularly when there are multiple stakeholders? In an initial exploration of this problem, we ask users what their ideas of fair treatment in recommendation might be, and why. We analyze what might cause discrepancies or changes between user's opinions towards fairness to eventually help inform the design of fairer and more transparent recommendation algorithms.
