论文标题
使用可解释的AI评估预测算法中的性别偏差
Assessing Gender Bias in Predictive Algorithms using eXplainable AI
论文作者
论文摘要
预测算法具有强大的潜力，可以在医学或教育等各种领域提供好处。但是，这些算法及其使用的数据是由人类构建的，因此，它们可以继承人类中存在的偏见和偏见。结果可能会系统地重复产生不公平结果的错误，甚至可能导致歧视情况（例如性别，社会或种族）。为了说明用多样化的培训数据集进行计数以避免偏见的重要性，我们操纵了众所周知的面部表达识别数据集，以探索性别偏见并讨论其含义。
Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial). In order to illustrate how important is to count with a diverse training dataset to avoid bias, we manipulate a well-known facial expression recognition dataset to explore gender bias and discuss its implications.
