论文标题
统计公平：公平分类目标
Statistical Equity: A Fairness Classification Objective
论文作者
论文摘要
机器学习系统已被证明可以传播过去的社会错误。鉴于此，大量研究重点是设计“公平”的解决方案。即使有很多工作，也没有对公平性的单一定义，主要是因为公平是主观的和背景依赖的。我们提出了一个新的公平定义，该定义是由公平原则的动机，该定义考虑了数据中的现有偏见，并试图做出公平的决策来解释这些先前的历史偏见。我们正式对公平的定义进行正式的定义，并以其适当的环境来激励它。接下来，我们将其操作以进行公平分类。我们执行多种自动和人类评估，以显示我们定义的有效性，并证明其在公平方面（例如反馈循环）的实用性。
Machine learning systems have been shown to propagate the societal errors of the past. In light of this, a wealth of research focuses on designing solutions that are "fair." Even with this abundance of work, there is no singular definition of fairness, mainly because fairness is subjective and context dependent. We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases. We formalize our definition of fairness, and motivate it with its appropriate contexts. Next, we operationalize it for equitable classification. We perform multiple automatic and human evaluations to show the effectiveness of our definition and demonstrate its utility for aspects of fairness, such as the feedback loop.
