机器学习中对主观意见的不公平
Unfairness towards subjective opinions in Machine Learning
摘要：
尽管学术界和工业界对机器学习（ML）有很高的兴趣，但与ML应用于现实生活问题相关的许多问题尚未得到解决。在这里，我们提出了一个限制，这是由于缺乏ML模型和数据集对特定应用的适应性。我们将不公平的新概念正式定义为排斥意见。我们提出了量化这种不公平的方法，并通过可视化帮助理解其原因。这些对基于ML的系统的功能的见解暗示了减轻不公平的方法。
英文摘要：
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML models and datasets to specific applications. We formalise a new notion of unfairness as exclusion of opinions. We propose ways to quantify this unfairness, and aid understanding its causes through visualisation. These insights into the functioning of ML-based systems hint at methods to mitigate unfairness.
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