Algorithmic care and consent: reframing ethical data mining in health-care systems

Abstract

Purpose This study aims to critically examine the ethical challenges posed by artificial intelligence (AI)-driven data mining in health care, particularly focusing on the limitations of traditional consent and regulatory frameworks. It proposes a reimagined model of care ethics and informed consent that is relational, participatory and justice-oriented. By introducing the “Ethical Data Mining Compass,” the paper seeks to guide health-care stakeholders in implementing ethical, transparent and inclusive AI systems that prioritize patient autonomy, accountability and data justice in clinical decision-making and digital health governance. Design/methodology/approach This conceptual paper uses a normative ethical analysis to explore the intersection of algorithmic decision-making and health-care data practices. Drawing from interdisciplinary literature in bioethics, data justice and care theory, it critiques existing regulatory frameworks and consent models. The study develops the “Ethical Data Mining Compass” as a theoretical framework by synthesizing principles of dynamic consent, participatory governance and relational ethics. This approach offers a structured lens for evaluating and guiding the ethical deployment of AI technologies in health-care systems, with particular attention to equity, transparency and patient-centered accountability. Findings The study finds that conventional models of informed consent and existing regulatory frameworks like General Data Protection Regulation and Health Insurance Portability and Accountability Act are insufficient to address the ethical complexities of AI-driven health care. It highlights that algorithmic opacity and asymmetrical power dynamics undermine patient autonomy and trust. The proposed “Ethical Data Mining Compass” offers a novel framework that integrates dynamic consent, data justice and participatory governance. This model enables more inclusive, transparent and accountable data practices, reframing consent as an ongoing, relational process. It positions ethical care as central to responsible AI implementation, ensuring that technological innovation aligns with patient rights and social equity. Originality/value This paper offers a novel contribution by reconceptualizing informed consent in the context of AI-driven health care through the lens of care ethics and data justice. Unlike traditional models that treat consent as a one-time transactional act, it introduces the “Ethical Data Mining Compass” as a dynamic and participatory framework. The study bridges gaps between ethical theory, health informatics and governance, providing a structured approach to guide responsible AI use in clinical settings. Its value lies in promoting a patient-centered, equitable and context-sensitive model for data ethics that addresses current regulatory and practical shortcomings in digital health systems.

Ähnliche Arbeiten