Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems

• URL: http://arxiv.org/abs/2409.06916v1
• Date: Tue, 10 Sep 2024 23:58:27 GMT
• Title: Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems
• Authors: Yongsu Ahn, Quinn K Wolter, Jonilyn Dick, Janet Dick, Yu-Ru Lin,
• Abstract summary: 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 affect their recommendations.
• Score: 3.990406494980651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis [69.37718774071793]
  This paper introduces novel information-theoretic measures for understanding recommender systems. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics.
  arXiv  Detail & Related papers  (2024-10-03T13:02:07Z)
• ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System [0.0]
  outlier detection is a key research area in recommender systems. We propose an approach that addresses these challenges by employing various clustering algorithms. Our experimental results demonstrate that this approach significantly improves the accuracy of outlier detection in recommender systems.
  arXiv  Detail & Related papers  (2024-10-01T05:06:07Z)
• Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
  In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied. We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
  arXiv  Detail & Related papers  (2024-04-19T16:45:50Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• Learning from a Learning User for Optimal Recommendations [43.2268992294178]
  We formalize a model to capture "learning users" and design an efficient system-side learning solution. We prove that the regret of RAES deteriorates gracefully as the convergence rate of user learning becomes worse. Our study provides a novel perspective on modeling the feedback loop in recommendation problems.
  arXiv  Detail & Related papers  (2022-02-03T22:45:12Z)
• Measuring Recommender System Effects with Simulated Users [19.09065424910035]
  Popularity bias and filter bubbles are two of the most well-studied recommender system biases. We offer a simulation framework for measuring the impact of a recommender system under different types of user behavior.
  arXiv  Detail & Related papers  (2021-01-12T14:51:11Z)
• Generative Inverse Deep Reinforcement Learning for Online Recommendation [62.09946317831129]
  We propose a novel inverse reinforcement learning approach, namely InvRec, for online recommendation. InvRec extracts the reward function from user's behaviors automatically, for online recommendation.
  arXiv  Detail & Related papers  (2020-11-04T12:12:25Z)
• Presentation of a Recommender System with Ensemble Learning and Graph Embedding: A Case on MovieLens [3.8848561367220276]
  Group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems. This study was performed on the MovieLens datasets, and the obtained results indicated the high efficiency of the presented method.
  arXiv  Detail & Related papers  (2020-07-15T12:52:15Z)
• Fairness-Aware Explainable Recommendation over Knowledge Graphs [73.81994676695346]
  We analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users. We propose a fairness constrained approach via re-ranking to mitigate this problem in the context of explainable recommendation over knowledge graphs.
  arXiv  Detail & Related papers  (2020-06-03T05:04:38Z)
• Empowering Active Learning to Jointly Optimize System and User Demands [70.66168547821019]
  We propose a new active learning approach that jointly optimize the active learning system (training efficiently) and the user (receiving useful instances) We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
  arXiv  Detail & Related papers  (2020-05-09T16:02:52Z)
• Modeling and Counteracting Exposure Bias in Recommender Systems [0.0]
  We study the bias inherent in widely used recommendation strategies such as matrix factorization. We propose new debiasing strategies for recommender systems. Our results show that recommender systems are biased and depend on the prior exposure of the user.
  arXiv  Detail & Related papers  (2020-01-01T00:12:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.