Can gene editing and agroecology be complementary? Various formulations of this question now animate debates over the future of food systems, including in the UN Committee on World Food Security and at the UN Food Systems Summit. Previous analyses have discussed the risks of gene editing for agroecosystems, smallholders, and the concentration of wealth by and for agro-industry. This paper takes a different approach, unpacking the epistemic, socioeconomic, and ontological politics inherent in complementarity. I ask: How is complementarity understood? Who (...) is asking and defining this question? What are the politics of entertaining the debate at all? I sketch the epistemic foundations of science and technology that organize different notions of evidence used in agroecology and genetic engineering. On this base, I offer 8 angles on the compatibility question, exploring the historical contradictions that complementarity discourses reveal and the contemporary work they do. I work through questions of technological neutrality, “root cause” problems, working with nature, encoding racism, and dilemmas of ownership and access. These questions, I argue, require a reckoning with ontologies of coloniality-modernity, which help us get underneath—and beyond—the complementarity question. Finally, I offer a framework for thinking about and working toward technology sovereignty. (shrink)

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory (...) approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

It has recently been argued that reproductive genetic manipulation technologies like mitochondrial replacement and germline CRISPR modifications cannot be said to save anyone’s life because, counterfactually, no one would suffer more or die sooner absent the intervention. The present article argues that, on the contrary, reproductive genetic manipulations may be life-saving (and, from this, have therapeutic value) under an appropriate population health perspective. As such, popular reports of reproductive genetic manipulations potentially saving lives or preventing disease are not necessarily mistaken, (...) though such terminology still requires further empirical validation. (shrink)