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Explaining the Unexplainable: Medical Decision-Making, AI, and a Right To Explanation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Explaining the Unexplainable: Medical Decision-Making, AI, and a Right To Explanation.
作者:	Lang, Michael Brian.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	129 p.
附註:	Source: Masters Abstracts International, Volume: 84-05.
附註:	Advisor: Zawati, Ma'n Hilmi;Beaudry, Jonas-Sebastien.
Contained By:	Masters Abstracts International84-05.
標題:	Medical equipment.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30157848
ISBN:	9798352989593

Explaining the Unexplainable: Medical Decision-Making, AI, and a Right To Explanation.
Lang, Michael Brian.

Explaining the Unexplainable: Medical Decision-Making, AI, and a Right To Explanation. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 129 p.

Source: Masters Abstracts International, Volume: 84-05.

Thesis (LL.M.)--McGill University (Canada), 2022.

This item must not be sold to any third party vendors.

Significant decisions in medicine are being increasingly delegated to machines. Automated machine learning models, many of which are thought to be at least as reliable and accurate as human decision-makers, are being used to make decisions about diagnosis, treatment, and care allocation. Though these systems will potentially enhance the quality of health outcomes and contribute to more efficient models of care delivery, they also pose an explanation challenge.Decisions made by machine learning models are often not accompanied by explanations: it is often technically impossible to know why a machine learning system reaches one decision rather than another. This raises difficult legal and ethical questions about responsibility, equality, and the fundamental principles of procedural law.This essay explores the degree to which unexplainable decision-making interferes with our conventional ways of understanding the practice and regulation of medicine. I suggest, using medical malpractice as a model, that the challenges posed by unexplainable machine learning may be profound. I describe how, in the face of unexplainable machine learning, several jurisdictions have enacted ‘rights to explanation,’ including Quebec and the European Union. But these emerging statutory rights are unlikely to respond adequately to the justice implications generated by unexplainable machine learning in medicine. In fact, rights to explanation will probably make things worse.

ISBN: 9798352989593Subjects--Topical Terms:

942653
Medical equipment.

Explaining the Unexplainable: Medical Decision-Making, AI, and a Right To Explanation.
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520 $a Significant decisions in medicine are being increasingly delegated to machines. Automated machine learning models, many of which are thought to be at least as reliable and accurate as human decision-makers, are being used to make decisions about diagnosis, treatment, and care allocation. Though these systems will potentially enhance the quality of health outcomes and contribute to more efficient models of care delivery, they also pose an explanation challenge.Decisions made by machine learning models are often not accompanied by explanations: it is often technically impossible to know why a machine learning system reaches one decision rather than another. This raises difficult legal and ethical questions about responsibility, equality, and the fundamental principles of procedural law.This essay explores the degree to which unexplainable decision-making interferes with our conventional ways of understanding the practice and regulation of medicine. I suggest, using medical malpractice as a model, that the challenges posed by unexplainable machine learning may be profound. I describe how, in the face of unexplainable machine learning, several jurisdictions have enacted ‘rights to explanation,’ including Quebec and the European Union. But these emerging statutory rights are unlikely to respond adequately to the justice implications generated by unexplainable machine learning in medicine. In fact, rights to explanation will probably make things worse.
520 $a Les decisions importantes en medecine sont de plus en plus deleguees aux machines. Des modeles d’apprentissage machine automatises, dont beaucoup sont consideres comme au moins aussi fiables et precis que les decideurs humains, sont utilises pour prendre des decisions en matiere de diagnostic, de traitement et de repartition des soins. Bien que ces systemes soient susceptibles d’ameliorer la qualite des resultats en matiere de sante et de contribuer a des modeles plus efficaces de prestation de soins, ils posent egalement un probleme d'explication.Les decisions prises par les modeles d'apprentissage automatique ne sont souvent pas accompagnees d'explications: il est souvent techniquement impossible de savoir pourquoi un systeme d'apprentissage automatique parvient a une decision plutot qu'a une autre. Cela souleve des questions juridiques et ethiques difficiles sur la responsabilite, l’egalite et les principes fondamentaux du droit procedural.Cette these explore la mesure dans laquelle la prise de decision inexplicable interfere avec nos facons conventionnelles de comprendre la pratique et la reglementation de la medecine. Je suggere, en utilisant la faute medicale comme modele, que les defis poses par l’apprentissage automatique inexplicable sont susceptibles d’etre profonds. Je decris comment, face a l’apprentissage automatique inexplicable, plusieurs juridictions ont promulgue des ‘droits a l’explication,’ notamment le Quebec et l’Union europeenne. Mais il est peu probable que ces droits statutaires emergents repondent de maniere adequate aux implications de justice generees par l’apprentissage automatique inexplicable en medecine. En fait, les droits a l’explication sont susceptibles d’aggraver la situation.
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