Augmenting Intelligence: A Hybrid Framework for Scalable and Stable Explanations

TL;DR

This paper introduces a hybrid explanation framework that combines automated and human-defined rules to improve scalability and stability in Explainable AI, demonstrated on customer churn prediction with high accuracy and reduced human effort.

Contribution

It proposes the Hybrid LRR-TED framework leveraging the 'Asymmetry of Discovery' concept, integrating automated safety rules with minimal human input for stable, scalable explanations.

Findings

Achieved 94% predictive accuracy with minimal human annotation.

Outperformed full manual rule-based explanations while halving human effort.

Validated on customer churn prediction, demonstrating practical effectiveness.

Abstract

Current approaches to Explainable AI (XAI) face a "Scalability-Stability Dilemma." Post-hoc methods (e.g., LIME, SHAP) may scale easily but suffer from instability, while supervised explanation frameworks (e.g., TED) offer stability but require prohibitive human effort to label every training instance. This paper proposes a Hybrid LRR-TED framework that addresses this dilemma through a novel "Asymmetry of Discovery." When applied to customer churn prediction, we demonstrate that automated rule learners (GLRM) excel at identifying broad "Safety Nets" (retention patterns) but struggle to capture specific "Risk Traps" (churn triggers)-a phenomenon we term the Anna Karenina Principle of Churn. By initialising the explanation matrix with automated safety rules and augmenting it with a Pareto-optimal set of just four human-defined risk rules, our approach achieves 94.00% predictive accuracy. This configuration outperforms the full 8-rule manual expert baseline while reducing human annotation effort by 50%, proposing a shift in the paradigm for Human-in-the-Loop AI: moving experts from the role of "Rule Writers" to "Exception Handlers."