{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T18:24:26Z","timestamp":1787509466285,"version":"build-2736575974"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>As artificial intelligence systems are increasingly used to guide decisions, it is essential that they follow ethical principles. A core principle in medicine is nonmaleficence, often equated with \u201cdo no harm\u201d. A formal definition of harm based on counterfactual reasoning has been proposed and popularized. This notion of harm has been promoted in simple settings with binary treatments and outcomes. Here, we highlight a problem with this definition in settings involving multiple treatment options. Illustrated by an example with 3 tuberculosis treatments (say, A, B, and C), we demonstrate that the counterfactual definition of harm can produce intransitive results: B is less harmful than A, C is less harmful than B, yet C is more harmful than A when compared pairwise. This intransitivity poses a challenge as it may lead to practical (clinical) decisions that are difficult to justify or defend. In contrast, an interventionist definition of harm based on expected utility forgoes counterfactual comparisons and ensures transitive treatment rankings.<\/jats:p>","DOI":"10.1093\/aje\/kwag064","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:57:21Z","timestamp":1773910641000},"page":"2230-2236","source":"Crossref","is-referenced-by-count":2,"title":["Counterfactual harm: a counter-argument"],"prefix":"10.1093","volume":"195","author":[{"given":"Amit N","family":"Sawant","sequence":"first","affiliation":[{"name":"Institute of Mathematics, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne , Lausanne, 1015, VD ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mats J","family":"Stensrud","sequence":"additional","affiliation":[{"name":"Institute of Mathematics, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne , Lausanne, 1015, VD ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"issue":"2","key":"2026080421264396400_ref1","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1136\/bmjqs-2024-017476","article-title":"Artificial intelligence-powered chatbots in search engines: a cross-sectional study on the quality and risks of drug information for patients","volume":"34","author":"Andrikyan","year":"2024","journal-title":"BMJ Qual Safety"},{"issue":"6","key":"2026080421264396400_ref2","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1093\/aje\/kwad162","article-title":"Perspective on\u2019harm\u2019 in personalized medicine","volume":"194","author":"Sarvet","year":"2025","journal-title":"Am J Epidemiol"},{"issue":"548","key":"2026080421264396400_ref3","doi-asserted-by":"publisher","first-page":"3045","DOI":"10.1080\/01621459.2023.2300507","article-title":"Policy learning with asymmetric counterfactual utilities","volume":"119","author":"Ben-Michael","year":"2024","journal-title":"J Am Stat Assoc"},{"key":"2026080421264396400_ref4","doi-asserted-by":"crossref","first-page":"15996","DOI":"10.52202\/068431-1164","article-title":"What\u2019s the harm? 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