Bill Gates believes AI-driven genetic engineering may revolutionize modern healthcare through rapid, effective, equitable healthcare solutions, as he spoke at the American Association for the Advancement of Science’s annual meeting this month. AI per Gates can accelerate such engineering breakthroughs as CRISPR-based HIV or malaria cures, which can have massive benefits for lower income countries where such research typically neglect to favor richer nations’ R&D. Aside from the good of health equity (cf. Utilitarian, social contract, or natural law-based virtue ethics supporting maximizing the benefit of just distribution of such benefits), how can ethics guide the balance between AI’s benefit and genetic engineering’s risk? If much of AI (particularly deep learning) has black box aspects that make it difficult to understand let alone replicate how a solution was generated, how can its various approaches be made reliable/transparent enough to guide genetic engineering interventions that may have unintended consequences for individuals and generations through unforeseen mutations/effects (particularly as they can permanently change humanity’s genetic code)?
GSAS Chief Data Scientist (Dominique Monlezun, MD, PhD, PhD, MPH) is AI+MD: our lead voice advocating for patients through ethical and effective AI, from the world’s first doctoral-level dual-trained physician-data scientist and ethicist. Dr. Monlezun regularly breaks down the latest AI advances and their ethical aspects to challenge/support scientists, health professionals, policy makers, and politicians to unite and protect patients and vulnerable patient populations.
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