Authors :
Chandan Kumar Sah; Dr. Lian Xiaoli; Muhammad Mirajul Islam
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/2n6a46xc
Scribd :
http://tinyurl.com/976rpb5c
DOI :
https://doi.org/10.5281/zenodo.10469839
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.
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.
