A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health

2026-07-27

AI Summary

This study addresses the lack of verifiable ethical safeguards in existing AI-driven digital phenotyping systems that leverage financial behavior data for mental health assessment, which are vulnerable to risks concerning informed consent, privacy, and fairness. To bridge this gap, the authors propose a computational ethics framework that formalizes ethical principles as deontic temporal logic constraints. By integrating the Z3 SMT solver, the framework enables machine-verifiable, real-time compliance checks. It further incorporates an ethics agent and counterexample generation mechanism to dynamically monitor and detect violations. Evaluated in a finance–mental health use case, the framework successfully eliminates scenarios violating specified ethical properties, thereby transcending conventional static, post-hoc compliance documentation and establishing a foundation for continuous, auditable ethical assurance in AI systems.

Abstract

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.

Problem

• digital phenotyping
• ethical governance
• AI ethics
• financial data
• mental health

Innovation

• computational ethics
• deontic temporal logic
• digital phenotyping
• formal verification
• ethical AI