Break Out of a Pigeonhole: A Unified Framework for Examining
Miscalibration, Bias, and Stereotype in Recommender Systems
- URL: http://arxiv.org/abs/2312.17443v1
- Date: Fri, 29 Dec 2023 02:32:12 GMT
- Title: Break Out of a Pigeonhole: A Unified Framework for Examining
Miscalibration, Bias, and Stereotype in Recommender Systems
- Authors: Yongsu Ahn and Yu-Ru Lin
- Abstract summary: This study aims to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues.
We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects.
Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.
- Score: 6.209548319476692
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite the benefits of personalizing items and information tailored to
users' needs, it has been found that recommender systems tend to introduce
biases that favor popular items or certain categories of items, and dominant
user groups. In this study, we aim to characterize the systematic errors of a
recommendation system and how they manifest in various accountability issues,
such as stereotypes, biases, and miscalibration. We propose a unified framework
that distinguishes the sources of prediction errors into a set of key measures
that quantify the various types of system-induced effects, both at the
individual and collective levels. Based on our measuring framework, we examine
the most widely adopted algorithms in the context of movie recommendation. Our
research reveals three important findings: (1) Differences between algorithms:
recommendations generated by simpler algorithms tend to be more stereotypical
but less biased than those generated by more complex algorithms. (2) Disparate
impact on groups and individuals: system-induced biases and stereotypes have a
disproportionate effect on atypical users and minority groups (e.g., women and
older users). (3) Mitigation opportunity: using structural equation modeling,
we identify the interactions between user characteristics (typicality and
diversity), system-induced effects, and miscalibration. We further investigate
the possibility of mitigating system-induced effects by oversampling
underrepresented groups and individuals, which was found to be effective in
reducing stereotypes and improving recommendation quality. Our research is the
first systematic examination of not only system-induced effects and
miscalibration but also the stereotyping issue in recommender systems.
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