Testing for discrimination consists of deriving a profile, known as the comparator, similar to the profile making the discrimination claim, known as the complainant, and comparing the outcomes of these two profiles. An important aspect for establishing discrimination is evidence, often obtained via discrimination testing tools that implement the complainant-comparator pair. In this work, we revisit the role of the comparator in discrimination testing. We argue for the causal modeling nature of deriving the comparator, and introduce a two-kinds classification for the comparator: the ceteris paribus (CP), and mutatis mutandis (MM) comparators. The CP comparator is the standard one among discrimination testing, representing an idealized comparison as it aims for having a complainant-comparator pair that only differs on membership to the protected attribute. As an alternative to it, we define the MM comparator, which requires that the comparator represents what would have been of the complainant without the effects of the protected attribute on the non-protected attributes. The complainant-comparator pair, in that case, may also be dissimilar in terms of all attributes. We illustrate these two comparators and their impact on discrimination testing using a real illustrative example. Importantly, we position generative models and, overall, machine learning methods as useful tools for constructing the MM comparator and, in turn, achieving more complex and realistic comparisons when testing for discrimination.

翻译：歧视测试的核心在于构建一个被称为比较对象的特征剖面，该剖面与提出歧视主张的申诉人特征剖面相似，并比较这两个剖面的结果。确立歧视的关键在于证据，通常通过实现申诉人-比较对象对的歧视测试工具获得。本研究重新审视了比较对象在歧视测试中的作用。我们论证了构建比较对象本质上是一种因果建模过程，并提出了比较对象的二元分类法：其他条件不变（CP）比较对象与变其所当变（MM）比较对象。CP比较对象是歧视测试中的标准范式，代表一种理想化比较，其目标是构建仅在受保护属性成员资格上存在差异的申诉人-比较对象对。作为其替代方案，我们定义了MM比较对象，它要求比较对象能表征申诉人在消除受保护属性对非受保护属性影响后的潜在状态。在此情况下，申诉人-比较对象对可能在所有属性维度上均存在差异。我们通过真实案例阐释了这两种比较对象及其对歧视测试的影响。重要的是，我们将生成模型及整体机器学习方法定位为构建MM比较对象的有力工具，从而在歧视测试中实现更复杂且更贴近现实的比较。