Unifying Bias and Unfairness in Information Retrieval: A Survey of
  Challenges and Opportunities with Large Language Models
Sunhao Dai Chen Xu Shicheng Xu Liang Pang Zhenhua Dong Jun Xu
With the rapid advancement of large language models (LLMs), information
retrieval (IR) systems, such as search engines and recommender systems, have
undergone a significant paradigm shift. This evolution, while heralding new
opportunities, introduces emerging challenges, particularly in terms of biases
and unfairness, which may threaten the information ecosystem. In this paper, we
present a comprehensive survey of existing works on emerging and pressing bias
and unfairness issues in IR systems when the integration of LLMs. We first
unify bias and unfairness issues as distribution mismatch problems, providing a
groundwork for categorizing various mitigation strategies through distribution
alignment. Subsequently, we systematically delve into the specific bias and
unfairness issues arising from three critical stages of LLMs integration into
IR systems: data collection, model development, and result evaluation. In doing
so, we meticulously review and analyze recent literature, focusing on the
definitions, characteristics, and corresponding mitigation strategies
associated with these issues. Finally, we identify and highlight some open
problems and challenges for future work, aiming to inspire researchers and
stakeholders in the IR field and beyond to better understand and mitigate bias
and unfairness issues of IR in this LLM era. We also consistently maintain a
GitHub repository for the relevant papers and resources in this rising
direction at https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey.