Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems

TL;DR

This paper presents an interactive visualization tool that helps users understand and explore the impacts of algorithmic biases in recommender systems, promoting transparency and fairness.

Contribution

The study introduces a novel interactive tool combining visualizations and counterfactual explanations to analyze biases in recommender systems, informed by user interviews.

Findings

Increases transparency of recommender system biases

Empowers users and researchers to explore bias impacts

Supports development of fairer recommendation algorithms

Abstract

Recommender systems have become integral to digital experiences, shaping user interactions and preferences across various platforms. Despite their widespread use, these systems often suffer from algorithmic biases that can lead to unfair and unsatisfactory user experiences. This study introduces an interactive tool designed to help users comprehend and explore the impacts of algorithmic harms in recommender systems. By leveraging visualizations, counterfactual explanations, and interactive modules, the tool allows users to investigate how biases such as miscalibration, stereotypes, and filter bubbles affect their recommendations. Informed by in-depth user interviews, this tool benefits both general users and researchers by increasing transparency and offering personalized impact assessments, ultimately fostering a better understanding of algorithmic biases and contributing to more equitable recommendation outcomes. This work provides valuable insights for future research and practical applications in mitigating bias and enhancing fairness in machine learning algorithms.