ACRE: Accelerating Random Forests for Explainability

As machine learning models become more widespread, they are being increasingly applied in applications that heavily impact people’s lives (e.g., medical diagnoses, judicial system sentences, etc.). Several communities are thus calling for ML models to be not only accurate, but also explainable. To achieve this, recommendations must be augmented with explanations summarizing how each recommendation outcome is derived. Explainable Random Forest (XRF) models are popular choices in this space, as they are both very accurate and can be augmented with explainability functionality, allowing end-users to learn how and why a specific outcome was reached. However, the limitations of XRF models hamper their adoption, the foremost being the high computational demands associated with training such models to support high-accuracy classifications, while also annotating them with explainability meta-data.