Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909648575004672 |
|---|---|
| author | Peña, Alejandro Fierrez, Julian Morales, Aythami Mancera, Gonzalo Lopez, Miguel Tolosana, Ruben |
| author_facet | Peña, Alejandro Fierrez, Julian Morales, Aythami Mancera, Gonzalo Lopez, Miguel Tolosana, Ruben |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment Peña, Alejandro Fierrez, Julian Morales, Aythami Mancera, Gonzalo Lopez, Miguel Tolosana, Ruben Artificial Intelligence Computation and Language 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. |
| title | Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.11880 |