Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions
复制标题
DOI：
--
复制
发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
Shira Mitchell;E. Potash;Solon Barocas
中科院分区：
文献类型：
--
作者：
Shira Mitchell;E. Potash;Solon Barocas
最近一系列研究活动试图定量定义基于统计和机器学习（ML）预测的决策的“公平性”。这个新领域的快速发展导致了广泛的不一致的术语和符号，对编目和比较定义提出了严峻的挑战。本文试图带来急需的秩序。首先，我们解释了各种选择和假设——通常是隐含的——来证明使用基于预测的决策是合理的。接下来，我们展示了这些选择和假设如何引起对公平性的关注，并从ML文献中提供了一个符号一致的公平性定义目录。在此过程中，我们为基于预测的决策系统的选择、假设和公平性考虑提供了一个简明的参考。
A recent flurry of research activity has attempted to quantitatively define "fairness" for decisions based on statistical and machine learning (ML) predictions. The rapid growth of this new field has led to wildly inconsistent terminology and notation, presenting a serious challenge for cataloguing and comparing definitions. This paper attempts to bring much-needed order. First, we explicate the various choices and assumptions made---often implicitly---to justify the use of prediction-based decisions. Next, we show how such choices and assumptions can raise concerns about fairness and we present a notationally consistent catalogue of fairness definitions from the ML literature. In doing so, we offer a concise reference for thinking through the choices, assumptions, and fairness considerations of prediction-based decision systems.