Tree-based machine learning models, such as decision trees and random
forests, have been hugely successful in classification tasks primarily because
of their predictive power in supervised learning tasks and ease of
interpretation. Despite their popularity and power, these models have been
found to produce unexpected or discriminatory outcomes. Given their
overwhelming success for most tasks, it is of interest to identify sources of
their unexpected and discriminatory behavior. However, there has not been much
work on understanding and debugging tree-based classifiers in the context of
fairness.
We introduce FairDebugger, a system that utilizes recent advances in machine
unlearning research to identify training data subsets responsible for instances
of fairness violations in the outcomes of a random forest classifier.
FairDebugger generates top-$k$ explanations (in the form of coherent training
data subsets) for model unfairness. Toward this goal, FairDebugger first
utilizes machine unlearning to estimate the change in the tree structures of
the random forest when parts of the underlying training data are removed, and
then leverages the Apriori algorithm from frequent itemset mining to reduce the
subset search space. We empirically evaluate our approach on three real-world
datasets, and demonstrate that the explanations generated by FairDebugger are
consistent with insights from prior studies on these datasets.

通过利用机器反学习方法，FairDebugger 能够识别造成随机森林分类器结果不公平的训练数据子集，并生成前 k 个解释（以一致的训练数据子集形式）来解释模型的不公平行为。

利用机器去学习的方式为随机森林提供基于实例的解释

Example-based Explanations for Random Forests using Machine Unlearning

The vast majority of techniques to train fair models require access to the
protected attribute (e.g., race, gender), either at train time or in
production. However, in many important applications this protected attribute is
largely unavailable. In this paper, we develop methods for measuring and
reducing fairness violations in a setting with limited access to protected
attribute labels. Specifically, we assume access to protected attribute labels
on a small subset of the dataset of interest, but only probabilistic estimates
of protected attribute labels (e.g., via Bayesian Improved Surname Geocoding)
for the rest of the dataset. With this setting in mind, we propose a method to
estimate bounds on common fairness metrics for an existing model, as well as a
method for training a model to limit fairness violations by solving a
constrained non-convex optimization problem. Unlike similar existing
approaches, our methods take advantage of contextual information --
specifically, the relationships between a model's predictions and the
probabilistic prediction of protected attributes, given the true protected
attribute, and vice versa -- to provide tighter bounds on the true disparity.
We provide an empirical illustration of our methods using voting data. First,
we show our measurement method can bound the true disparity up to 5.5x tighter
than previous methods in these applications. Then, we demonstrate that our
training technique effectively reduces disparity while incurring lesser
fairness-accuracy trade-offs than other fair optimization methods with limited
access to protected attributes.

在有限的受保护属性标签访问情况下，本文开发了测量和减少公平违规的方法，提出了估计现有模型的常见公平度量和解决约束非凸优化问题以限制公平违规的模型训练技术，并且相比其他受限受保护属性的公平优化方法，在减小公平度的同时减少了公平性和准确性的权衡。