Across various sectors such as healthcare, criminal justice, national
security, finance, and technology, large-scale machine learning (ML) and
artificial intelligence (AI) systems are being deployed to make critical
data-driven decisions. Many have asked if we can and should trust these ML
systems to be making these decisions. Two critical components are prerequisites
for trust in ML systems: interpretability, or the ability to understand why the
ML system makes the decisions it does, and fairness, which ensures that ML
systems do not exhibit bias against certain individuals or groups. Both
interpretability and fairness are important and have separately received
abundant attention in the ML literature, but so far, there have been very few
methods developed to directly interpret models with regard to their fairness.
In this paper, we focus on arguably the most popular type of ML interpretation:
feature importance scores. Inspired by the use of decision trees in knowledge
distillation, we propose to leverage trees as interpretable surrogates for
complex black-box ML models. Specifically, we develop a novel fair feature
importance score for trees that can be used to interpret how each feature
contributes to fairness or bias in trees, tree-based ensembles, or tree-based
surrogates of any complex ML system. Like the popular mean decrease in impurity
for trees, our Fair Feature Importance Score is defined based on the mean
decrease (or increase) in group bias. Through simulations as well as real
examples on benchmark fairness datasets, we demonstrate that our Fair Feature
Importance Score offers valid interpretations for both tree-based ensembles and
tree-based surrogates of other ML systems.

我们提出了一种公平特征重要性评分，用于解释决策树等可解释的黑盒机器学习模型对公平性或偏见的贡献。通过模拟和真实示例，我们证明了该方法对树形集成和其他机器学习系统的树形替代物提供了有效的解释性。

基于树模型和替代模型的公平特征重要性评分

Fair Feature Importance Scores for Interpreting Tree-Based Methods and  Surrogates

Interpretable surrogates of black-box predictors trained on high-dimensional
tabular datasets can struggle to generate comprehensible explanations in the
presence of correlated variables. We propose a model-agnostic interpretable
surrogate that provides global and local explanations of black-box classifiers
to address this issue. We introduce the idea of concepts as intuitive groupings
of variables that are either defined by a domain expert or automatically
discovered using correlation coefficients. Concepts are embedded in a surrogate
decision tree to enhance its comprehensibility. First experiments on FRED-MD, a
macroeconomic database with 134 variables, show improvement in
human-interpretability while accuracy and fidelity of the surrogate model are
preserved.

我们提出了一种模型无关的可解释替代方案，用于全局和局部解释黑盒分类器。该替代方案使用相关系数自动发现变量的直观分组，并将其嵌入到替代决策树中，以提高其可理解性。在宏观经济数据库上的实验结果表明，在保持替代模型准确性和保真度的同时，该方案提高了人类可解释性。