Augmenting Intelligence: A Hybrid Framework for Scalable and Stable Explanations

2025-12-22

AI Summary

XAI faces a fundamental trade-off between scalability and stability: post-hoc methods (e.g., LIME, SHAP) scale well but yield unstable explanations, while supervised frameworks (e.g., TED) offer stability at the cost of heavy reliance on manual annotations. To resolve this, we propose LRR-TED—a hybrid framework integrating Generalized Linear Rule Models (GLRMs) with TED. It automatically discovers domain-agnostic “retention patterns” via rule learning and introduces two novel principles—“discovery asymmetry” and the “lost Anna Karenina principle”—enabling experts to annotate only critical churn-triggering rules, thereby shifting their role from rule authors to anomaly responders. Leveraging Pareto-optimal rule selection and dual-track modeling (safety net vs. risk trap), LRR-TED achieves 94.00% accuracy in customer churn prediction—outperforming eight handcrafted rule baselines while reducing annotation effort by 50%.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

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."

Problem

• Addresses the scalability-stability dilemma in Explainable AI
• Proposes a hybrid framework to reduce human annotation effort
• Shifts experts from rule writers to exception handlers

Innovation

• Hybrid framework combining automated and human-defined rules
• Asymmetry of Discovery principle for scalable stable explanations
• Reduces human effort by shifting experts to exception handlers