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.

翻译：生成式人工智能的兴起，尤其是大语言模型（LLMs），使得在关注准确性的同时审视公平性变得愈发重要。近期研究已开始探讨LLMs在推荐等领域中的公平性评估。由于个性化是推荐系统的内在特征，将其纳入公平性评估至关重要。然而，当前公平性评估框架对个性化的考量程度尚不明确。我们的综合文献综述旨在填补这一空白，通过考察现有框架如何处理LLMs的公平性评估，重点关注个性化因素的整合。尽管我们详尽收集并分析了相关研究，但发现大多数评估忽视了个性化这一推荐系统的关键方面，从而在无意中延续了不公平的实践。我们的发现揭示了这一疏漏，并强调了亟需开发更细致、承认个性化的公平性评估方法。此类改进对于推动人工智能社区内的公平发展至关重要。