Developing Interpretable Models for Complex Decision-Making

This study addresses the challenge of constructing interpretable machine-learning models for complex decision-making processes. Using techniques like Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive explanations, as well as new algorithmic approaches that attempt to keep a balance, between model complexity versus interpretability. A transparency-enhancing technique, grounded in Information Bottleneck theory, improves interpretability without penalizing predictive performance.The article also introduces a live anti-discrimination approach through which disparate impact and equal opportunity gaps can be resolved in AI-based decision-making. These models have been practically shown in the deployment of an Interactive Decision Support System (IDSS) operating at 95% correct diagnosis and very high acceptance by users when tested with patients from healthcare settings.Uncovering such results, for interpretable models in high-stakes domains, not only shows promise of interpretability but provides a starting point that could guide future work to improve transparency and fairness odds across AI. The research has far-reaching implications for ethical AI deployment, serving as a strong basis for the future growth of responsible AI systems within all industries..