Motivated by the growing importance of reducing unfairness in ML predictions, Fair-ML researchers have presented an extensive suite of algorithmic "fairness-enhancing" remedies. Most existing algorithms, however, are agnostic to the sources of the observed unfairness. As a result, the literature currently lacks guiding frameworks to specify conditions under which each algorithmic intervention can potentially alleviate the underpinning cause of unfairness. To close this gap, we scrutinize the underlying biases (e.g., in the training data or design choices) that cause observational unfairness. We present a bias-injection sandbox tool to investigate fairness consequences of various biases and assess the effectiveness of algorithmic remedies in the presence of specific types of bias. We call this process the bias(stress)-testing of algorithmic interventions. Unlike existing toolkits, ours provides a controlled environment to counterfactually inject biases in the ML pipeline. This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark. In particular, we can test whether a given remedy can alleviate the injected bias by comparing the predictions resulting after the intervention in the biased setting with true labels in the unbiased regime -- that is, before any bias injection. We illustrate the utility of our toolkit via a proof-of-concept case study on synthetic data. Our empirical analysis showcases the type of insights that can be obtained through our simulations.
翻译:由于减少最低生活水平预测中的不公平现象的重要性日益增加,Fair-ML研究人员提出了一套广泛的算法“加强公平性”补救措施,但大多数现有的算法对所观察到的不公平现象的来源是不可知的。因此,文献目前缺乏指导框架来说明每一种算法干预在哪些条件下可以减轻不公平现象的根本原因。为了缩小这一差距,我们仔细检查导致观察不公的基本偏差(例如培训数据或设计选择),我们提出了一个偏见注射沙箱工具,以调查各种偏差的公平后果,并在存在特定类型的偏见的情况下评估算法补救措施的有效性。我们把这一过程称为对所观察到的不公平现象的来源的偏差(压力)检验。因此,与现有的工具包不同,我们的文献缺乏一种控制环境,以抵消ML管道中的偏差。为了缩小这一差距,我们这个结构提供了超越观察数据或设计选择的公平性衡量干预的明显能力。我们特别可以测试一种特定补救措施能否通过比较在采用特定类型的偏差的情况下对预测结果的偏差性进行评估。我们称之为对算法干预后产生的偏差性的检验。我们用任何模拟数据的精确性分析,我们用工具来说明我们的精确性分析。我们的方法是用工具的正确性分析。我们的任何解释。