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.

翻译：基于深度神经网络的自动推荐系统在过去十年中变得极为流行。然而，其中一些系统可能被用于欧盟《人工智能法案》中列为高风险的应用领域，例如在线求职者推荐。当在欧盟使用时，此类商用人工智能系统需具备适当的统计特性，以防范其可能引发的潜在歧视。这推动了我们的研究贡献，我们提出了一种新颖的最优运输策略，用于缓解多类神经网络分类中不良的算法偏见。该策略与模型无关，可应用于任何多类神经网络分类模型。为提前应对使用文本数据的推荐系统的认证需求，我们在Bios数据集上进行了测试，该数据集的学习任务是基于领英简历预测男性和女性个体的职业。结果表明，与标准策略相比，该方法在此场景下能将不良算法偏见降低至更低的水平。