Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment

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.

MINT-Demo: Membership Inference Test Demonstrator

We present the Membership Inference Test Demonstrator, to emphasize the need for more transparent machine learning training processes. MINT is a technique for experimentally determining whether certain data has been used during the training of machine learning models. We conduct experiments with popular face recognition models and 5 public databases containing over 22M images. Promising results, up to 89% accuracy are achieved, suggesting that it is possible to recognize if an AI model has been trained with specific data. Finally, we present a MINT platform as demonstrator of this technology aimed to promote transparency in AI training.

Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs

This work adapts and studies the gradient-based Membership Inference Test (gMINT) to the classification of text based on LLMs. MINT is a general approach intended to determine if given data was used for training machine learning models, and this work focuses on its application to the domain of Natural Language Processing. Using gradient-based analysis, the MINT model identifies whether particular data samples were included during the language model training phase, addressing growing concerns about data privacy in machine learning. The method was evaluated in seven Transformer-based models and six datasets comprising over 2.5 million sentences, focusing on text classification tasks. Experimental results demonstrate MINTs robustness, achieving AUC scores between 85% and 99%, depending on data size and model architecture. These findings highlight MINTs potential as a scalable and reliable tool for auditing machine learning models, ensuring transparency, safeguarding sensitive data, and fostering ethical compliance in the deployment of AI/NLP technologies.

Is my Data in your AI Model? Membership Inference Test with Application to Face Images

This paper introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if specific data was used during the training of Artificial Intelligence (AI) models. Specifically, we propose two novel MINT architectures designed to learn the distinct activation patterns that emerge when an audited model is exposed to data used during its training process. The first architecture is based on a Multilayer Perceptron (MLP) network and the second one is based on Convolutional Neural Networks (CNNs). The proposed MINT architectures are evaluated on a challenging face recognition task, considering three state-of-the-art face recognition models. Experiments are carried out using six publicly available databases, comprising over 22 million face images in total. Also, different experimental scenarios are considered depending on the context available of the AI model to test. Promising results, up to 90% accuracy, are achieved using our proposed MINT approach, suggesting that it is possible to recognize if an AI model has been trained with specific data.