Monitoring Robustness and Individual Fairness
This addresses the need for trustworthy AI decision-making by complementing offline methods, though it is incremental as it builds on existing fixed-radius nearest neighbor search techniques.
The paper tackles the problem of monitoring input-output robustness and individual fairness in deployed black-box AI models by proposing runtime monitors that detect when similar inputs lead to dissimilar outputs, demonstrating effectiveness in detecting violations on standard benchmarks.
Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI models that make decisions about humans. We propose runtime monitoring of input-output robustness of deployed, black-box AI models, where the goal is to design monitors that would observe one long execution sequence of the model, and would raise an alarm whenever it is detected that two similar inputs from the past led to dissimilar outputs. This way, monitoring will complement existing offline ``robustification'' approaches to increase the trustworthiness of AI decision-makers. We show that the monitoring problem can be cast as the fixed-radius nearest neighbor (FRNN) search problem, which, despite being well-studied, lacks suitable online solutions. We present our tool Clemont, which offers a number of lightweight monitors, some of which use upgraded online variants of existing FRNN algorithms, and one uses a novel algorithm based on binary decision diagrams -- a data-structure commonly used in software and hardware verification. We have also developed an efficient parallelization technique that can substantially cut down the computation time of monitors for which the distance between input-output pairs is measured using the $L_\infty$ norm. Using standard benchmarks from the literature of adversarial and semantic robustness and individual fairness, we perform a comparative study of different monitors in \tool, and demonstrate their effectiveness in correctly detecting robustness violations at runtime.