论文标题
通过实验公平：A/B测试中的不平等作为负责任的设计方法
Fairness through Experimentation: Inequality in A/B testing as an approach to responsible design
论文作者
论文摘要
随着技术的不断发展，人们对个人被抛在后面的关注越来越大。许多企业都在努力采用负责任的设计实践，并避免其产品和服务的任何意外后果，从隐私脆弱性到算法偏见。我们提出了一种基于实验的新型方法，以实现公平和包容性。我们之所以使用实验，是因为我们不仅要评估产品和算法的内在特性，还要评估它们对人的影响。我们通过引入A/B测试的不平等方法来做到这一点，从而利用经济学文献的阿特金森指数。我们展示了如何对这种不平等程度进行因果推断。我们还介绍了整个现场不平等影响的概念，该概念捕获了针对实验特定亚群的包容性影响，并展示了如何对这种影响进行统计推断。我们提供了LinkedIn的真实示例，以及对Atkinson索引的计算的开源，高度可扩展的实现及其在Spark/Scala中的差异。我们还提供了一年多的学习价值 - 通过大规模部署我们的方法并分析了数千个实验 - 在哪些领域以及哪些产品创新似乎固有地通过包容性促进了公平性。
As technology continues to advance, there is increasing concern about individuals being left behind. Many businesses are striving to adopt responsible design practices and avoid any unintended consequences of their products and services, ranging from privacy vulnerabilities to algorithmic bias. We propose a novel approach to fairness and inclusiveness based on experimentation. We use experimentation because we want to assess not only the intrinsic properties of products and algorithms but also their impact on people. We do this by introducing an inequality approach to A/B testing, leveraging the Atkinson index from the economics literature. We show how to perform causal inference over this inequality measure. We also introduce the concept of site-wide inequality impact, which captures the inclusiveness impact of targeting specific subpopulations for experiments, and show how to conduct statistical inference on this impact. We provide real examples from LinkedIn, as well as an open-source, highly scalable implementation of the computation of the Atkinson index and its variance in Spark/Scala. We also provide over a year's worth of learnings -- gathered by deploying our method at scale and analyzing thousands of experiments -- on which areas and which kinds of product innovations seem to inherently foster fairness through inclusiveness.
