Practical Guidelines for Building Explainable, Efficient, and Robust Machine Learning Systems

The growing integration of machine learning (ML) into critical applications has elevated the importance of models that are not only accurate but also \textit{explainable}, \textit{efficient}, and \textit{robust}. While each of these properties has been extensively studied in isolation, their simultaneous realization remains a formidable and largely unresolved challenge. This paper presents a comprehensive exploration of the theoretical, algorithmic, and empirical foundations for constructing ML systems that jointly satisfy these three desiderata. We begin by formalizing the multi-objective learning framework, introducing mathematical formulations that capture the trade-offs among interpretability, computational parsimony, and resilience to perturbations. We then survey a wide spectrum of algorithmic strategies, including sparse modeling, distillation, adversarial training, modular architectures, and multi-objective optimization techniques. Through detailed empirical evaluations across domains such as healthcare, autonomous driving, finance, and natural language processing, we quantify the interdependencies and tensions among the triadic objectives. Our results highlight that no single solution dominates across all metrics, but careful design choices can yield models that approach Pareto-optimal performance in practical settings. Building on these insights, we propose a set of system-level design principles for deploying trustworthy ML, including modularization, continuous monitoring, and human-centered explanation interfaces. We conclude with an agenda for future research, calling for unified theoretical frameworks, domain-aware evaluation protocols, and interdisciplinary collaboration to advance the field toward more transparent, resilient, and accessible AI.