Advancing Interpretable and Robust Machine Learning for High-Stakes Applications

This paper presents a critical review of recent advances in interpretable and robust machine learning for high-stakes applications. As machine learning systems are increasingly deployed in domains such as healthcare, finance, and autonomous systems, ensuring trustworthiness has become essential. We analyze the limitations of black-box models, particularly their lack of transparency and vulnerability to adversarial conditions. The study surveys key interpretability techniques, including feature attribution, model simplification, and post-hoc explanation methods. In parallel, robustness strategies such as adversarial training, uncertainty estimation, and distributional resilience are examined. We highlight the trade-offs between interpretability, accuracy, and robustness in real-world scenarios. Furthermore, the paper discusses evaluation metrics and benchmarks used to assess trustworthy AI systems. Case studies demonstrate how these approaches perform under high-risk conditions. The review identifies current research gaps and challenges in achieving scalable and reliable solutions. Finally, we outline future directions toward building transparent, resilient, and accountable machine learning systems.