Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era
Author:
Affiliation:
1. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China
2. CAS Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
3. Huawei Noah's Ark Lab, Shenzhen, China
Funder
National Natural ScienceFoundation of China (NSFC)
Youth Innova-tion Promotion Association CAS
National Key R&D Program of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671458
Reference205 articles.
1. Abdollahpouri et al. 2020. Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction (2020).
2. Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv (2019).
3. Abubakar Abid Maheen Farooqi and James Zou. 2021. Persistent anti-muslim bias in large language models. In AAAI.
4. Qingyao Ai Ting Bai et al. 2023. Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community. AI Open (2023).
5. Ekin Akyürek Tolga Bolukbasi et al. 2022. Towards tracing factual knowledge in language models back to the training data. arXiv (2022).
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