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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explaining the Unexplainable :/
Reminder of title:	Medical Decision-Making, AI, and a Right To Explanation.
Author:	Lang, Michael Brian.
Description:	1 online resource (129 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-05.
Contained By:	Masters Abstracts International84-05.
Subject:	Medical equipment. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30157848click for full text (PQDT)
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. - 1 online resource (129 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352989593Subjects--Topical Terms:

3560831
Medical equipment.
Index Terms--Genre/Form:

542853
Electronic books.

Explaining the Unexplainable : = Medical Decision-Making, AI, and a Right To Explanation.
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245 1 0 $a Explaining the Unexplainable : $b Medical Decision-Making, AI, and a Right To Explanation.
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300 $a 1 online resource (129 pages)
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500 $a Source: Masters Abstracts International, Volume: 84-05.
500 $a Advisor: Zawati, Ma'n Hilmi; Beaudry, Jonas-Sebastien.
502 $a Thesis (LL.M.)--McGill University (Canada), 2022.
504 $a Includes bibliographical references
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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538 $a Mode of access: World Wide Web
650 4 $a Medical equipment. $3 3560831
650 4 $a Medical malpractice. $3 3682980
650 4 $a Decision making. $3 517204
650 4 $a Law. $3 600858
650 4 $a Medical ethics. $3 526828
650 4 $a Health care management. $3 2122906
650 4 $a Medicine. $3 641104
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710 2 $a McGill University (Canada). $3 1018122
773 0 $t Masters Abstracts International $g 84-05.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30157848 $z click for full text (PQDT)