Achieving Explainable, Scalable, and Robust Machine Learning for Real-World Applications

The increasing deployment of machine learning systems in high-stakes and resource-constrained environments has accentuated the necessity for models that are simultaneously explainable, scalable, and robust. While each of these desiderata has been extensively studied in isolation, their integration remains a critical open challenge due to inherent trade-offs and complex interactions. This paper presents a comprehensive framework that unifies theoretical foundations, methodological advances, and empirical evaluations to address these intertwined objectives. We formalize explainability through function decomposition and feature attribution, characterize scalability in terms of computational efficiency and statistical generalization, and define robustness via distributional and adversarial perturbations. Our survey of contemporary methods reveals a rich design space including regularization techniques, modular architectures, and robust optimization paradigms that can be systematically combined to achieve balanced performance. Extensive experiments across diverse datasets demonstrate Pareto-optimal trade-offs and highlight practical considerations for model selection and deployment. Finally, we discuss ethical implications, contextual constraints, and future research directions aimed at developing trustworthy machine learning systems that align with human values and operational demands.