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Break Out of a Pigeonhole: A Unified Framework for Examining
  Miscalibration, Bias, and Stereotype in Recommender Systems

Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems

29 December 2023
Yongsu Ahn
Yu-Ru Lin
    CML
ArXivPDFHTML

Papers citing "Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems"

2 / 2 papers shown
Title
Interactive Counterfactual Exploration of Algorithmic Harms in
  Recommender Systems
Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems
Yongsu Ahn
Quinn K. Wolter
Jonilyn Dick
Janet Dick
Yu-Ru Lin
HAI
37
0
0
10 Sep 2024
How Algorithmic Confounding in Recommendation Systems Increases
  Homogeneity and Decreases Utility
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
A. Chaney
Brandon M Stewart
Barbara E. Engelhardt
CML
169
313
0
30 Oct 2017
1