用约束编程优化医疗AI伦理风险，平衡安全与合规。
Optimizing Ethical Risk Reduction for Medical Intelligent Systems with Constraint Programming
- 将伦理风险优化建模为约束规划问题，寻找最佳风险评估分配。
- 对比MIP、SAT和CP三种方法，发现CP在表达与可扩展性上更优。
- 适合医疗AI合规团队及可信AI风险管理者参考应用。
医疗智能系统（MIS）日益融入医疗流程，虽带来显著效益，但亦引发重大安全与伦理关切。根据欧盟《人工智能法案》，多数MIS将被归类为高风险系统，需通过正式的风险管理流程确保符合可信AI的伦理要求。本文聚焦于伦理考量下的风险减缓优化问题，旨在通过最优分配风险评估值，在覆盖可信AI伦理要求的前提下实现风险最小化。我们将该问题形式化为约束优化任务，并研究了三种求解范式：混合整数规划（MIP）、可满足性（SAT）与约束编程（CP）。贡献包括该优化问题的数学建模、基于Minizinc语言的模型构建，以及三类方法在性能、表达能力与可扩展性上的比较实验。基于方法局限性，本文提出将Minizinc模型集成至完整可信AI伦理风险管理流程的未来方向。
原文摘要 · Abstract (English)
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 trustworthy 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 Programming(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 approach 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.
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