English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The rise of generative artificial intelligence, particularly Large LanguageModels (LLMs), has intensified the imperative to scrutinize fairness alongsideaccuracy. Recent studies have begun to investigate fairness evaluations forLLMs within domains such as recommendations. Given that personalization is anintrinsic aspect of recommendation systems, its incorporation into fairnessassessments is paramount. Yet, the degree to which current fairness evaluationframeworks account for personalization remains unclear. Our comprehensiveliterature review aims to fill this gap by examining how existing frameworkshandle fairness evaluations of LLMs, with a focus on the integration ofpersonalization factors. Despite an exhaustive collection and analysis ofrelevant works, we discovered that most evaluations overlook personalization, acritical facet of recommendation systems, thereby inadvertently perpetuatingunfair practices. Our findings shed light on this oversight and underscore theurgent need for more nuanced fairness evaluations that acknowledgepersonalization. Such improvements are vital for fostering equitabledevelopment within the AI community.

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