Interpretable Machine Learning Models for Enhanced Decision Support Systems

Abstract

Interpretable machine learning models are gaining significant attention due to their potential to augment decision-making processes across various domains. This paper investigates the critical role of interpretable models in enhancing decision support systems. By bridging the gap between accuracy and comprehensibility, interpretable models offer transparency and insight into complex AI systems, enabling stakeholders to trust, validate, and understand the reasoning behind predictions or recommendations. This paper presents a comprehensive review of interpretability techniques, highlighting their applicability in different domains. Furthermore, it examines the trade-offs between model interpretability and performance, emphasizing the need for balancing accuracy with transparency. Practical case studies and methodologies are explored to demonstrate the utility and effectiveness of interpretable models in real-world scenarios. Finally, the paper outlines future research directions to propel the integration of interpretable machine learning models into decision support systems for improved transparency and user acceptance.