论文标题
低估机器学习中的偏见和不足
Underestimation Bias and Underfitting in Machine Learning
论文作者
论文摘要
通常，机器学习中称为算法偏见的原因是培训数据中的历史性偏见。但是有时可能会通过算法本身引入（或至少加剧）偏见。算法实际上可以强调偏见的方式并没有引起很多关注，研究人员直接关注消除偏见的方法 - 无论是什么来源。在本文中，我们报告了初步研究，以了解导致分类算法偏见的因素。我们认为这很重要，因为低估偏见与正规化密不可分，即解决过度拟合的措施可能会突出偏见。
Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by the algorithm itself. The ways in which algorithms can actually accentuate bias has not received a lot of attention with researchers focusing directly on methods to eliminate bias - no matter the source. In this paper we report on initial research to understand the factors that contribute to bias in classification algorithms. We believe this is important because underestimation bias is inextricably tied to regularization, i.e. measures to address overfitting can accentuate bias.
