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<p>Research on Artificial Intelligence (AI) is lined with moral considerations. In healthcare, a high-risk field, sub-fields have emerged to mitigate AI-specific ethical issues such as fairness and transparency. However, similar considerations remain unaddressed beyond healthcare, and as generative AI tools (‘<i>GenAI</i>’) reach lay audiences, this neglect yields ethical concerns. The present work focuses on learning from ethical considerations in healthcare to mitigate challenges of <i>GenAI</i>. We structure our proposed mitigation strategies around three of the five established biomedical and AI ethics principles (autonomy, transparency, beneficence), highlighting the risks GenAI poses for intellectual property owners (scraping of copyrighted data, unpaid labor, plagiarism, fraud). We propose concrete ways to affirm these principles on GenAI, using biomedical AI examples and the emerging frameworks they have sparked in domains of data ownership, federated learning, and data provenance. This article comes at a pivotal time for AI, generalizing ethics-aware principles to GenAI to open new research avenues toward responsible AI.</p>

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Consentful-by-design: a perspective on safeguarding data ownership from generative AI leveraging lessons from the healthcare domain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

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

摘要

Research on Artificial Intelligence (AI) is lined with moral considerations. In healthcare, a high-risk field, sub-fields have emerged to mitigate AI-specific ethical issues such as fairness and transparency. However, similar considerations remain unaddressed beyond healthcare, and as generative AI tools (‘GenAI’) reach lay audiences, this neglect yields ethical concerns. The present work focuses on learning from ethical considerations in healthcare to mitigate challenges of GenAI. We structure our proposed mitigation strategies around three of the five established biomedical and AI ethics principles (autonomy, transparency, beneficence), highlighting the risks GenAI poses for intellectual property owners (scraping of copyrighted data, unpaid labor, plagiarism, fraud). We propose concrete ways to affirm these principles on GenAI, using biomedical AI examples and the emerging frameworks they have sparked in domains of data ownership, federated learning, and data provenance. This article comes at a pivotal time for AI, generalizing ethics-aware principles to GenAI to open new research avenues toward responsible AI.