Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment
This addresses ethical concerns in high-stake applications like recruitment, but it is incremental as it builds on existing bias mitigation strategies.
The study tackled the problem of demographic biases in LLMs, particularly in AI-based recruitment, by proposing a privacy-enhancing framework that reduces gender information to mitigate biased behaviors, with experiments showing it effectively prevents systems from reproducing data biases.
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.