用于心理健康的金融数字表型分析的计算伦理框架
A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health
- University College Dublin（都柏林大学学院）
- Dublin City University（都柏林城市大学）
- Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对人工智能驱动数字表型系统伦理治理难题，提出计算伦理框架，将伦理要求形式化为道义时态逻辑约束，通过金融数据和心理健康案例研究，用Z3求解器建模验证关键伦理属性，实现持续机器可验证伦理检查，支持相关系统发展。
AI中文摘要：
人工智能驱动系统的伦理治理常通过高层次原则和静态文档来表达，这在监管要求和系统级验证间造成差距。在数字表型分析中此挑战尤为严峻，因连续行为数据引发了对同意、隐私和公平性的担忧。本文提出一个用于人工智能驱动数字表型系统的计算伦理框架，将伦理要求形式化为道义时态逻辑约束，还有一个监督系统的概念性伦理主体，确保受监督系统满足指定约束。通过涉及金融数据和心理健康的案例研究，用Z3可满足性模理论（SMT）求解器对关键伦理属性建模并验证。评估表明该框架逻辑一致，能通过基于反例的验证排除违反指定伦理属性的情况。此为早期研究，实现了持续的、机器可验证的伦理检查，超越了基于静态文档的追溯合规。我们讨论了局限性，包括需用数据进行现实世界验证、主观性和情境敏感性挑战、需人工监督等，并概述了此类方法如何支持具有持续和可审计伦理保障的数字表型分析及人工智能系统的发展。
英文摘要：
Ethical governance of AI-driven systems is often expressed through high-level principles and static documentation, creating a gap between regulatory requirements and system-level verification. This challenge is particularly acute in digital phenotyping, where continuous behavioural data raises concerns around consent, privacy, and fairness. In this paper, we propose a computational ethical framework for AI-driven digital phenotyping system in which ethical requirements are formalised as deontic temporal logic constraints, alongside a conceptual ethical agent that oversees the system and ensures that any supervised system satisfies the specified constraints. Using a case study involving financial data and mental health, we model key ethical properties and verify them using the Z3 Satisfiability Modulo Theories (SMT) solver. Our evaluation shows that the framework is logically consistent and that violations of the specified ethical properties are ruled out within the formal model through counterexample-based verification. This presents early research enabling continuous, machine-verifiable ethical checking, moving beyond retrospective compliance based on static documentation. We discuss limitations, including the need for real-world verification with data, the challenge with subjectivity and contextual sensitivity, the need for human oversight, and outline how such approaches can support the development of digital phenotyping and AI systems with continuous and auditable ethical guarantees.
