Conclusive Local Interpretation Rules for Random Forests
This addresses the need for interpretability in random forests used in critical domains like discrimination and safety, though it builds incrementally on prior work.
The authors tackled the problem of interpreting random forest decisions in high-stakes applications by introducing LionForests, a technique that provides conclusive rules as explanations, validated through sensitivity analysis and comparisons with state-of-the-art methods.
In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear interpretations for their decisions. Otherwise, their obscure decision-making processes can lead to socioethical issues as they interfere with people's lives. In the aforementioned sectors, random forest algorithms strive, thus their ability to explain themselves is an obvious requirement. In this paper, we present LionForests, which relies on a preliminary work of ours. LionForests is a random forest-specific interpretation technique, which provides rules as explanations. It is applicable from binary classification tasks to multi-class classification and regression tasks, and it is supported by a stable theoretical background. Experimentation, including sensitivity analysis and comparison with state-of-the-art techniques, is also performed to demonstrate the efficacy of our contribution. Finally, we highlight a unique property of LionForests, called conclusiveness, that provides interpretation validity and distinguishes it from previous techniques.