Creating fair AI systems is a complex problem that involves the assessment of
context-dependent bias concerns. Existing research and programming libraries
express specific concerns as measures of bias that they aim to constrain or
mitigate. In practice, one should explore a wide variety of (sometimes
incompatible) measures before deciding which ones warrant corrective action,
but their narrow scope means that most new situations can only be examined
after devising new measures. In this work, we present a mathematical framework
that distils literature measures of bias into building blocks, hereby
facilitating new combinations to cover a wide range of fairness concerns, such
as classification or recommendation differences across multiple multi-value
sensitive attributes (e.g., many genders and races, and their intersections).
We show how this framework generalizes existing concepts and present frequently
used blocks. We provide an open-source implementation of our framework as a
Python library, called FairBench, that facilitates systematic and extensible
exploration of potential bias concerns.

创建公平的 AI 系统是一个复杂的问题，涉及评估上下文相关的偏见问题。本文提出了一个数学框架，将偏见的文献度量指标化为构建模块，从而促进涵盖广泛公平问题的新组合，我们还提供了一个名为 FairBench 的 Python 库，用于有系统且可扩展地探索潜在的偏见问题。