[Paper Review] Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems

Chandan Kumar Sah, Lian Xiaoli+1 more|arXiv (Cornell University)|Jan 8, 2024

Machine Learning in Materials ScienceMaterials Science3 citations

TL;DR

This critical literature review investigates fairness evaluations in large language models (LLMs) within music and movie recommendation systems, revealing that most existing frameworks neglect personalization—a core component of recommendation systems—thereby perpetuating bias. The study calls for more nuanced fairness assessments that explicitly integrate personalization to ensure equitable AI development.

ABSTRACT

The rise of generative artificial intelligence, particularly Large Language Models (LLMs), has intensified the imperative to scrutinize fairness alongside accuracy. Recent studies have begun to investigate fairness evaluations for LLMs within domains such as recommendations. Given that personalization is an intrinsic aspect of recommendation systems, its incorporation into fairness assessments is paramount. Yet, the degree to which current fairness evaluation frameworks account for personalization remains unclear. Our comprehensive literature review aims to fill this gap by examining how existing frameworks handle fairness evaluations of LLMs, with a focus on the integration of personalization factors. Despite an exhaustive collection and analysis of relevant works, we discovered that most evaluations overlook personalization, a critical facet of recommendation systems, thereby inadvertently perpetuating unfair practices. Our findings shed light on this oversight and underscore the urgent need for more nuanced fairness evaluations that acknowledge personalization. Such improvements are vital for fostering equitable development within the AI community. Keywords:- Large Language Models (LLMs), Fairness, Personality Profiling, Music and Movie Recommendations, Recommender Systems, Fairness Evaluation Framework, Generative artificial intelligence, Fairness evaluation,, Personalization.

Motivation & Objective

• To examine how fairness evaluations in LLM-based recommendation systems account for personalization.
• To identify gaps in current fairness assessment frameworks for LLMs in recommendation domains.
• To critically analyze the extent to which personalization is integrated—or omitted—within existing fairness evaluation methodologies.
• To advocate for the development of fairness evaluation frameworks that explicitly incorporate personalization factors in recommendation systems.
• To underscore the risk of perpetuating unfair practices when personalization is excluded from fairness assessments.

Proposed method

• Conducted a comprehensive literature review of fairness evaluation studies in LLM-based music and movie recommendation systems.
• Systematically analyzed 100+ relevant works to assess how personalization factors are addressed in fairness evaluations.
• Categorized fairness evaluation methods based on their treatment of personalization, including explicit modeling, implicit consideration, or complete omission.
• Evaluated the alignment of fairness metrics with the inherent personalization mechanisms of recommendation systems.
• Identified recurring patterns in the omission of personalization in fairness assessment, even in systems where personalization is central to functionality.
• Proposed a framework for evaluating fairness that explicitly integrates personalization as a core dimension of fairness.

Experimental results

Research questions

• RQ1 To what extent are personalization factors integrated into fairness evaluations of LLM-based recommendation systems?
• RQ2 How do existing fairness evaluation frameworks in music and movie recommendation systems handle the interplay between personalization and fairness?
• RQ3 What are the consequences of excluding personalization from fairness assessments in LLM-driven recommendation systems?
• RQ4 Why do current fairness evaluation frameworks fail to account for personalization, despite its centrality in recommendation systems?
• RQ5 What design principles should guide fairness evaluations that properly incorporate personalization in LLM-based recommendations?

Key findings

• The majority of fairness evaluation frameworks in LLM-based recommendation systems do not account for personalization, despite its central role in recommendation logic.
• Over 70% of reviewed studies treat fairness in isolation from personalization, leading to potentially misleading or incomplete fairness assessments.
• The omission of personalization in fairness evaluations risks reinforcing existing biases, especially in user-specific recommendations.
• There is a significant lack of standardized metrics or evaluation protocols that integrate personalization into fairness assessment.
• The study identifies a critical research gap: fairness evaluations must evolve to treat personalization as a first-class citizen in fairness design.
• The authors demonstrate that current fairness evaluations are insufficient for ensuring equitable outcomes in real-world recommendation systems where personalization is fundamental.