# How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?

## TL;DR

This paper introduces a model-agnostic optimal transport method to reduce gender biases in multi-class neural network classifiers for job recommendation systems, ensuring fairness and compliance with AI regulations.

## Contribution

It presents a novel optimal transport strategy that mitigates gender biases in neural network classifiers, applicable across different models and datasets.

## Key findings

- Reduces gender bias more effectively than standard methods
- Applicable to textual data in job recommendation systems
- Improves fairness metrics in the Bios dataset

## Abstract

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications which are ranked as High Risk by the European Commission in the A.I. act, as for instance for online job candidate recommendation. When used in the European Union, commercial AI systems for this purpose will then be required to have to proper statistical properties with regard to potential discrimination they could engender. This motivated our contribution, where we present a novel optimal transport strategy to mitigate undesirable algorithmic biases in multi-class neural-network classification. Our stratey is model agnostic and can be used on any multi-class classification neural-network model. To anticipate the certification of recommendation systems using textual data, we then used it on the Bios dataset, for which the learning task consists in predicting the occupation of female and male individuals, based on their LinkedIn biography. Results show that it can reduce undesired algorithmic biases in this context to lower levels than a standard strategy.