Safety-critical decision-making often necessitates operating within complex and uncertain environments, typically characterized as partially-observable Multi-Agent Systems (MAS). Effective decision-making in these settings demands a profound understanding of the environment and accurate representation and quantification of epistemic uncertainty due to informational deficits. This uncertainty representation must reflect prior knowledge and dynamically integrate observational data. While Version Space Learning (VSL) offers a framework for set-based uncertainty representation, prior methods often depend on under-approximation of the actual version space, which falls short in safety-critical applications. We introduce a model checking-based VSL framework to enhance uncertainty representation and context-awareness in such applications. Our approach employs network of timed automata (NTA) to model the MAS environment, capturing its mechanisms and prior knowledge efficiently. The inherent non-determinism of timed automata facilitates an over-approximation of the actual version space, ensuring all plausible hypotheses are considered. We further refine the parameter ranges of the NTA using proof traces, optimizing the use of observational data. In a medical diagnosis case study, our framework identified a missing rule in a traditional rule-based system and provided interpretable and scalable results, demonstrating our method’s ability to improve decision-making accuracy and manage complex scenarios effectively.

Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and designed to detect fairness issues under static settings. However, many ML-enabled systems operate in a dynamic environment where the predictive decisions made by the system impact the environment, which in turn affects future decision-making. Such a self-reinforcing feedback loop can cause fairness violations in the long term, even if the immediate outcomes are fair. In this paper, we propose a simulation-based framework called Fairsenseto detect and analyze long-term unfairness in ML-enabled systems. Given a fairness requirement, Fairsenseperforms Monte-Carlo simulation to enumerate evolution traces for each system configuration. Then, Fairsenseperforms sensitivity analysis on the space of possible configurations to understand the impact of design options and environmental factors on the long-term fairness of the system. We demonstrate Fairsense'spotential utility through three real-world case studies: Loan lending, opioids risk scoring, and predictive policing.