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Bias and Mitigation in Large Language Models: Addressing Inequalities and Promoting Ethical AI Development
编号：128 访问权限：仅限参会人 更新：2025-12-23 13:12:30 浏览：100次 拓展类型2

报告开始：2025年12月30日 16:15（Asia/Amman）

报告时间：15min

所在会场：[S2] Track 2: IoT and applications [S2-2] Track 2: IoT and applications

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摘要
Large Language Models (LLMs) are used extensively in natural language processing but are biased and hence yield unfair output. In this paper, the bias present in four prominent models—BERT, XLNet, RoBERTa, and ALBERT—is examined using the Crows-Pairs dataset, which is employed for identifying biased language patterns. The paper discusses the working of the models and the type of their biases. Bias mitigation methods address various factors of bias through techniques such as Counterfactual Data Augmentation (CDA), Adversarial Debiasing, and the AI Fairness 360 Toolkit (AIF360), which aim to ensure fairness in AI systems. It aims to create more balanced and dependable AI systems, which will lead to the development of ethical and unbiased language models. The project also aims to showcase how training data, model architecture, and interventions outside of the model can be employed to prevent bias. By outlining how to avoid and identify bias, the article sets the stage for the potential future development of responsible AI. Through research, it has been determined that there is a need to continually probe and improve in light of changing needs, to develop equitable AI for all applications.
 
关键词
Large Language Models (LLMs), Bias, Mitigation, BERT, RoBERTa, XLNet, ALBERT, Stereotypical, anti-stereotypical.
报告人
Nagaraj P
Assistant Professor India； SRM Institute of Science and Technology；Department of Computer Science and Engineering； Tiruchirappalli； Tamil Nādu

稿件作者
Nagaraj P India； SRM Institute of Science and Technology；Department of Computer Science and Engineering； Tiruchirappalli； Tamil Nādu
Muneeswaran V Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, India
Ayman Amer Faculty of Engineering； Jordan； Zarqa Univeristy
Mohamed Hafez INTI-IU-University;Shinawatra University
Raja Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil, Virudhunagar, India.
Mohammad Tahidul Islam Australia；School of IT and Engineering Melbourne Institute of Technology Melbourne
Muhammad Fazal Ijaz Australia；Torrens University
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重要日期

  • 会议日期

    12月29日

    2025

    至

    12月31日

    2025

  • 12月30日 2025

    报告提交截止日期

  • 02月10日 2026

    初稿截稿日期

  • 02月10日 2026

    注册截止日期

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