Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations

• URL: http://arxiv.org/abs/2409.10825v1
• Date: Tue, 17 Sep 2024 01:37:57 GMT
• Title: Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations
• Authors: Shahnewaz Karim Sakib, Anindya Bijoy Das,
• Abstract summary: Large Language Model (LLM)-based recommendation systems provide more comprehensive recommendations than traditional systems. These systems often exhibit biases, favoring mainstream content while marginalizing non-traditional options due to skewed training data. This study investigates the intricate relationship between bias and LLM-based recommendation systems.
• Score: 3.5297361401370044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM)-based recommendation systems provide more comprehensive recommendations than traditional systems by deeply analyzing content and user behavior. However, these systems often exhibit biases, favoring mainstream content while marginalizing non-traditional options due to skewed training data. This study investigates the intricate relationship between bias and LLM-based recommendation systems, with a focus on music, song, and book recommendations across diverse demographic and cultural groups. Through a comprehensive analysis conducted over different LLM-models, this paper evaluates the impact of bias on recommendation outcomes. Our findings reveal that bias is so deeply ingrained within these systems that even a simpler intervention like prompt engineering can significantly reduce bias, underscoring the pervasive nature of the issue. Moreover, factors like intersecting identities and contextual information, such as socioeconomic status, further amplify these biases, demonstrating the complexity and depth of the challenges faced in creating fair recommendations across different groups.

Related papers

• Cognitive Biases in Large Language Models for News Recommendation [68.90354828533535]
  This paper explores the potential impact of cognitive biases on large language models (LLMs) based news recommender systems. We discuss strategies to mitigate these biases through data augmentation, prompt engineering and learning algorithms aspects.
  arXiv  Detail & Related papers  (2024-10-03T18:42:07Z)
• Mitigating Propensity Bias of Large Language Models for Recommender Systems [20.823461673845756]
  We introduce a novel framework named Counterfactual LLM Recommendation (CLLMR) We propose a spectrum-based side information encoder that implicitly embeds structural information from historical interactions into the side information representation. Our CLLMR approach explores the causal relationships inherent in LLM-based recommender systems.
  arXiv  Detail & Related papers  (2024-09-30T07:57:13Z)
• Social Debiasing for Fair Multi-modal LLMs [55.8071045346024]
  Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities. However, these models often inherit severe social biases from their training datasets, leading to unfair predictions based on attributes like race and gender. This paper addresses the issue of social biases in MLLMs by i) Introducing a comprehensive Counterfactual dataset with Multiple Social Concepts (CMSC) and ii) Proposing an Anti-Stereotype Debiasing strategy (ASD)
  arXiv  Detail & Related papers  (2024-08-13T02:08:32Z)
• CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models [58.57987316300529]
  Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks. To evaluate the biases exhibited by LLMs, researchers have recently proposed a variety of datasets. We propose CEB, a Compositional Evaluation Benchmark that covers different types of bias across different social groups and tasks.
  arXiv  Detail & Related papers  (2024-07-02T16:31:37Z)
• The African Woman is Rhythmic and Soulful: An Investigation of Implicit Biases in LLM Open-ended Text Generation [3.9945212716333063]
  Implicit biases are significant because they influence the decisions made by Large Language Models (LLMs) Traditionally, explicit bias tests or embedding-based methods are employed to detect bias, but these approaches can overlook more nuanced, implicit forms of bias. We introduce two novel psychological-inspired methodologies to reveal and measure implicit biases through prompt-based and decision-making tasks.
  arXiv  Detail & Related papers  (2024-07-01T13:21:33Z)
• A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System [9.470545149911072]
  This paper proposes a normative framework to benchmark consumer fairness in LLM-powered recommender systems. We argue that this gap can lead to arbitrary conclusions about fairness. Experiments on the MovieLens dataset on consumer fairness reveal fairness deviations in age-based recommendations.
  arXiv  Detail & Related papers  (2024-05-03T16:25:27Z)
• GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models [83.30078426829627]
  Large language models (LLMs) have gained popularity and are being widely adopted by a large user community. The existing evaluation methods have many constraints, and their results exhibit a limited degree of interpretability. We propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs to assess bias in models.
  arXiv  Detail & Related papers  (2023-12-11T12:02:14Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems [0.0]
  The bias amplification issue needs to be investigated while using these algorithms. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms.
  arXiv  Detail & Related papers  (2023-01-18T16:29:05Z)

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.