Example-based Explanations for Random Forests using Machine Unlearning
Tanmay Surve Romila Pradhan
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.