14. Januar 2026

What can generative AI learn from ethical frameworks developed in healthcare AI? The new article, “Consentful-by-design: a perspective on safeguarding data ownership from generative AI leveraging lessons from the healthcare domain,” explores how established biomedical and AI ethics principles can help address emerging ethical challenges of GenAI – such as intellectual property violations, unpaid labor, plagiarism, and fraud.

A brief TL;DR:
➡️ Key ethical risks of GenAI
the authors  highlight how GenAI systems pose risks to several groups of people, including intellectual property owners, through practices such as scraping copyrighted data, enabling plagiarism and fraud, and relying on unpaid labor.

➡️ Why healthcare AI ethics matter for GenAI
Healthcare AI is a high-risk domain where ethical principles, such as autonomy, transparency, and beneficence, are well-established and have been operationalized to address fairness, accountability, and trust.

➡️ A matchy match
Drawing on examples from biomedical AI, the authors propose concrete mitigation strategies and suggest utilizing emerging frameworks for data ownership, federated learning, and data provenance.

This paper brings together interdisciplinary perspectives from ethics, AI, and healthcare, aiming to open new research avenues toward responsible and ethics-aware generative AI.

Authors:

• Akis Linardos,
• Theresa Willem,
• Alena Buyx,
• Showkot Hossain,
• Taeho Jung,
• Dimitrios Makris &
• Spyridon Bakas

Read the full article here