EXPLAINABLE ROBUSTNESS FOR ADAPTIVE DECISION SUPPORT UNDER DEEP UNCERTAINTY
https://doi.org/10.59982/18294359-26.1-as-33
Decision-making in complex, non-stationary environments requires models that are not only mathematically robust but also transparent to human stakeholders. While Robust Optimization (RO) and Distributionally Robust Optimization (DRO) provide essential safeguards against worst-case scenarios and model misspecification, they often function as “black boxes”. This opacity creates a significant implementation paradox, i.e., the more sophisticated the robust model, the harder it is for human operators to justify its conservative choices, leading to automation bias or the “override trap”.
This paper proposes a comprehensive framework for Explainable Robustness (XR) to bridge the gap between algorithmic resilience and human trust. We introduce Vector-Valued Robust Markov Decision Processes (VV-RMDPs), which decompose scalar value functions into explicit safety and efficiency margins. This decomposition supports Contrastive Explanations that clearly identify the specific environmental risks targeted by a policy. To ensure these explanations remain reliable under perturbation, we incorporate “Formal Verification” via Lipschitz stability bounds. Furthermore, we define a Trust-Aware Index that quantifies the quality of explanations by integrating mathematical fidelity, interpretability, and ethical compliance. By transitioning robust decision-making from opaque protection to interpretable insight, this framework provides a path toward sustainable human-AI teaming in high-stakes domains such as healthcare, autonomous infrastructure, and public policy [Grigoryan].
Keywords: Explainable Robustness, Adaptive Decision Support, Deep Uncertainty, Trust-Aware Explainable AI (TAXAI), Robust Markov Decision Processes, Distributionally Robust Optimization, Vector-Valued Robust MDPs, Human-AI Collaboration.
PAGES : 403-409