Algorithmic predictions are increasingly used to inform the allocations of
goods and interventions in the public sphere. In these domains, predictions
serve as a means to an end. They provide stakeholders with insights into
likelihood of future events as a means to improve decision making quality, and
enhance social welfare. However, if maximizing welfare is the ultimate goal,
prediction is only a small piece of the puzzle. There are various other policy
levers a social planner might pursue in order to improve bottom-line outcomes,
such as expanding access to available goods, or increasing the effect sizes of
interventions.
Given this broad range of design decisions, a basic question to ask is: What
is the relative value of prediction in algorithmic decision making? How do the
improvements in welfare arising from better predictions compare to those of
other policy levers? The goal of our work is to initiate the formal study of
these questions. Our main results are theoretical in nature. We identify
simple, sharp conditions determining the relative value of prediction
vis-\`a-vis expanding access, within several statistical models that are
popular amongst quantitative social scientists. Furthermore, we illustrate how
these theoretical insights may be used to guide the design of algorithmic
decision making systems in practice.

在公共领域中，算法预测用于配置商品和干预措施，预测作为一种手段为相关利益相关者提供关于未来事件可能性的见解，以提高决策质量和增加社会福利，本研究的目标是正式研究预测在算法决策中的相对价值，并在几个量化社会科学研究者之间流行的统计模型中确定预测相对于扩大获取权的价值，并展示如何将这些理论见解用于指导实践中的算法决策系统设计。

算法决策中预测的相对价值

The Relative Value of Prediction in Algorithmic Decision Making

As virtually all aspects of our lives are increasingly impacted by
algorithmic decision making systems, it is incumbent upon us as a society to
ensure such systems do not become instruments of unfair discrimination on the
basis of gender, race, ethnicity, religion, etc. We consider the problem of
determining whether the decisions made by such systems are discriminatory,
through the lens of causal models. We introduce two definitions of group
fairness grounded in causality: fair on average causal effect (FACE), and fair
on average causal effect on the treated (FACT). We use the Rubin-Neyman
potential outcomes framework for the analysis of cause-effect relationships to
robustly estimate FACE and FACT. We demonstrate the effectiveness of our
proposed approach on synthetic data. Our analyses of two real-world data sets,
the Adult income data set from the UCI repository (with gender as the protected
attribute), and the NYC Stop and Frisk data set (with race as the protected
attribute), show that the evidence of discrimination obtained by FACE and FACT,
or lack thereof, is often in agreement with the findings from other studies. We
further show that FACT, being somewhat more nuanced compared to FACE, can yield
findings of discrimination that differ from those obtained using FACE.

本研究通过因果模型的视角考虑了决策系统是否基于性别、种族、宗教等方面会造成歧视的问题，引入了基于因果关系的群体公平的两个定义：FACE 和 FACT，提出了鲁宾 - 尼曼潜在结果框架来鲁棒地估算 FACE 和 FACT，并在合成数据上展示了方法的有效性。同时，作者还在真实数据集上进行了分析，发现 FACT 比 FACE 更微妙，在不同情形下可得出不同结论。

算法决策中的公平：通过因果镜头探究

Fairness in Algorithmic Decision Making: An Excursion Through the Lens  of Causality

Algorithmic decision making systems are ubiquitous across a wide variety of
online as well as offline services. These systems rely on complex learning
methods and vast amounts of data to optimize the service functionality,
satisfaction of the end user and profitability. However, there is a growing
concern that these automated decisions can lead, even in the absence of intent,
to a lack of fairness, i.e., their outcomes can disproportionately hurt (or,
benefit) particular groups of people sharing one or more sensitive attributes
(e.g., race, sex). In this paper, we introduce a flexible mechanism to design
fair classifiers by leveraging a novel intuitive measure of decision boundary
(un)fairness. We instantiate this mechanism with two well-known classifiers,
logistic regression and support vector machines, and show on real-world data
that our mechanism allows for a fine-grained control on the degree of fairness,
often at a small cost in terms of accuracy.

该研究引入了一种灵活的机制，以设计公平的分类器，其中利用了一种新颖的、直观的决策边界（不）公平度量，并结合实际的数据表明，该机制在维持准确度的小代价下，允许对公平度进行微调。