Machine Learning Robustness: New Challenges and Approaches

This article, part of the Vector Institute's Trustworthy AI series, addresses the critical importance of model robustness in machine learning governance. It highlights how organizations face challenges in adopting ML due to data complexity, costs, and uncertainty around risks, often leading to caution despite potential value. The piece defines model robustness as the stability of a model's performance when exposed to new data versus training data, emphasizing its role in maintaining trust and identifying issues like malicious attacks or biases. It outlines five key technical challenges for maintaining robustness: data quality, model decay, feature stability, precision versus recall trade-offs, and input perturbations. Aimed primarily at non-technical professionals, the article provides guidelines for integrating robustness into risk management frameworks. By understanding these elements, organizations can better monitor ML systems, mitigate reputational and financial risks, and maximize the value derived from AI implementations. The analysis serves as a practical checklist for governing ML models responsibly, ensuring they perform reliably and fairly in real-world applications.