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The Epistemology and Ethics of Compu...

Kawamleh, Suzanne.

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The Epistemology and Ethics of Computational Science for Decision Making.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Epistemology and Ethics of Computational Science for Decision Making./
作者:	Kawamleh, Suzanne.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	145 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
Contained By:	Dissertations Abstracts International85-01A.
標題:	Philosophy of science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30529019
ISBN:	9798379924249

The Epistemology and Ethics of Computational Science for Decision Making.
Kawamleh, Suzanne.

The Epistemology and Ethics of Computational Science for Decision Making. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 145 p.

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

Thesis (Ph.D.)--Indiana University, 2023.

My dissertation concerns the development and use of advanced computational methods to produce scientific evidence for ethical decision-making. Decision makers have a moral duty to make decisions that are adequately informed and justified by the available evidence. Scientific evidence is now produced by advanced computational methods, including artificial intelligence (AI) systems. I explore the impact of computational methods on evidence-based decision making in climate science, human medicine, and criminal justice. In the first chapter, I argue that the responsible use of climate model evidence requires that scientists test and establish the dynamical adequacy of the model that produces the evidence. A model is dynamically adequate if scientists can demonstrate that it adequately represents the climate processes behind a prediction and how those processes change over time, under conditions of climate change. I then argue that dynamically adequate models produce evidence that can justify climate mitigation and adaptation policies.In the second chapter, I examine the epistemic opacity of medical AI systems. Such opacity is presumed to violate basic principles of medical ethics, like informed consent. This has led to growing ethical demands for explainable AI in healthcare. To the contrary, I argue that such demands are misguided because explainability cannot transform AI outputs in the type of evidence that patients can use to make informed decisions about their medical care. In the third chapter, I use a Bayesian account of evidence to assess AI predictions of recidivism risk as evidence for or against hypotheses about a black and white defendant's probability of future rearrest. I first argue for a notion of equal treatment as requiring equally confirmatory evidence for punitive criminal justice decisions. I then show that such predictions provide weaker confirmatory evidence for a black defendant's future recidivism risk than a white defendant, thus violating equal treatment.

ISBN: 9798379924249Subjects--Topical Terms:

2079849
Philosophy of science.
Subjects--Index Terms:

Decision making

The Epistemology and Ethics of Computational Science for Decision Making.
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