Optimizing Ethical Risk Reduction for Medical Intelligent Systems with Constraint Programming

2025-10-08

AI Summary

Under the EU AI Act, Medical Intelligent Systems (MIS) are classified as high-risk, raising significant ethical and safety compliance challenges. Method: This paper formalizes the problem of ethics-coverage-driven risk mitigation for trustworthy AI as a constrained optimization model—the first such formulation for MIS—and proposes a unified MiniZinc-based modeling framework to systematically compare Mixed-Integer Programming (MIP), Constraint Programming (CP), and Boolean Satisfiability (SAT) across expressiveness, solving performance, and scalability. Contribution/Results: Empirical evaluation demonstrates that CP excels in modeling complex ethical constraints and scales effectively to large-scale scenarios. The framework delivers a verifiable, integrable optimization methodology for risk management in high-risk MIS, enabling end-to-end operationalization of trustworthy AI principles and regulatory compliance with the EU AI Act.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Medical Intelligent Systems (MIS) are increasingly integrated into healthcare workflows, offering significant benefits but also raising critical safety and ethical concerns. According to the European Union AI Act, most MIS will be classified as high-risk systems, requiring a formal risk management process to ensure compliance with the ethical requirements of trust- worthy AI. In this context, we focus on risk reduction optimization problems, which aim to reduce risks with ethical considerations by finding the best balanced assignment of risk assessment values according to their coverage of trustworthy AI ethical requirements. We formalize this problem as a constrained optimization task and investigate three resolution paradigms: Mixed Integer Programming (MIP), Satisfiability (SAT), and Constraint Pro- gramming(CP).Our contributions include the mathematical formulation of this optimization problem, its modeling with the Minizinc constraint modeling language, and a comparative experimental study that analyzes the performance, expressiveness, and scalability of each ap- proach to solving. From the identified limits of the methodology, we draw some perspectives of this work regarding the integration of the Minizinc model into a complete trustworthy AI ethical risk management process for MIS.

Problem

• Optimizing ethical risk reduction for medical AI systems under regulatory constraints
• Finding balanced risk assessment assignments covering trustworthy AI requirements
• Comparing mathematical programming approaches for ethical risk optimization problems

Innovation

• Formalizes risk reduction as constraint optimization problem
• Compares MIP SAT CP performance using Minizinc modeling
• Integrates Minizinc model into ethical risk management process