Understanding Bias in Machine Learning
Understanding Bias in Machine Learning
复制标题
了解机器学习中的偏差
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Daniela Oelke
中科院分区：
文献类型：
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作者：
Jindong Gu;Daniela Oelke
众所周知，偏见是在人力资源，公共部门，医疗保健等许多领域中公平决策的障碍。最近，希望使用机器学习方法来采取此类决策会减少甚至解决问题。同时，机器学习专家警告说，机器学习模型也可能会偏差。在本文中，我们的目标是从技术角度解释机器学习中的偏见问题，并说明偏见数据对机器学习模型的影响。为了达到这样的目标，我们开发了交互式图，以可视化从合成数据中学到的偏差。
Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop interactive plots to visualizing the bias learned from synthetic data.