论文标题
通过机器学习来加强社会偏见：信用评分观点
Societal biases reinforcement through machine learning: A credit scoring perspective
论文作者
论文摘要
机器学习和人工智能是否确保社会偏见蓬勃发展？本文旨在分析此问题。确实，正如算法通过数据所告知的那样，如果这些算法损坏了，从社会偏见的角度来看，良好的机器学习算法将从所提供的数据中学习并回顾有关与分类或意图的回归相关的预测模式所学的模式。换句话说，社会的行为方式是积极的还是负面的。在本文中，我们通过使用客户通过其应用程序提供的完全相同的信息来预测客户的性别或种族，分析如何从数据传输到银行贷款批准。
Does machine learning and AI ensure that social biases thrive ? This paper aims to analyse this issue. Indeed, as algorithms are informed by data, if these are corrupted, from a social bias perspective, good machine learning algorithms would learn from the data provided and reverberate the patterns learnt on the predictions related to either the classification or the regression intended. In other words, the way society behaves whether positively or negatively, would necessarily be reflected by the models. In this paper, we analyse how social biases are transmitted from the data into banks loan approvals by predicting either the gender or the ethnicity of the customers using the exact same information provided by customers through their applications.
