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Evidence record 13804 · automatically gathered

A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health

arXiv:2607.24275v1 Announce Type: cross 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 whi

Record details

Published: 28 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Regulation · Privacy · Healthcare
Retrieved: 28 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (28 July 2026), “A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health,” evidence record 13804, https://ethics.ai/record/13804 (originally published by arXiv cs.CY).

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