用隐私增强框架消除性别信息，让AI招聘更公平
Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment
- 在训练中移除性别信息，防止模型学习数据偏见
- 实验证明该方法能有效减少两个LLM在招聘中的性别偏见
- 适合关注AI公平性与伦理的从业者参考
近年来，大语言模型（LLMs）在高风险场景中的应用日益增多，但其仍面临性别、种族等人口统计学偏见、问责机制缺失和隐私泄露等伦理问题。本文以AI招聘为案例，分析基于Transformer的系统如何从数据中学习人口偏见，并提出一种隐私增强框架，在学习过程中主动去除性别信息，从而缓解最终模型的偏见行为。实验对比了两种不同LLM在数据偏见影响下的表现，结果表明所提框架能有效阻止模型复制训练数据中的性别偏见。
原文摘要 · Abstract (English)
The use of language technologies in high-stake settings is increasing in recent years, mostly motivated by the success of Large Language Models (LLMs). However, despite the great performance of LLMs, they are are susceptible to ethical concerns, such as demographic biases, accountability, or privacy. This work seeks to analyze the capacity of Transformers-based systems to learn demographic biases present in the data, using a case study on AI-based automated recruitment. We propose a privacy-enhancing framework to reduce gender information from the learning pipeline as a way to mitigate biased behaviors in the final tools. Our experiments analyze the influence of data biases on systems built on two different LLMs, and how the proposed framework effectively prevents trained systems from reproducing the bias in the data.
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