ethique_IA

Machine Learning and Ethics

Sat, 14 May 2022 08:23:24 +0000

Author Names: Mathiesen T.,Broekman M.

Database Source: Embase Weekly Updates

Journal Title: Acta Neurochirurgica, Supplementum

Article Title: Machine Learning and Ethics

Year: 2022

Issue:

Volume: 134

Abstract: When new technology is introduced into healthcare, novel ethical dilemmas arise in the human-machine interface. As artificial intelligence (AI), machine learning (ML) and big data can exhaust human oversight and memory capacity, this will give rise to many of these new dilemmas. Technology has little if any ethical status but is inevitably interwoven with human activity and thus may serve to allow qualitative and quantitative disruption of human performance and interaction. We argue that personal integrity, justice of resource allocation and accountability of moral agency comprise three themes that characterize ethical dilemmas that arise with development and application of AI. These themes are important to address in parallel to further evolution of AI in health care for ethical practice of healthcare.<br/>Copyright © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Promoting Ethical Deployment of Artificial Intelligence and Machine Learning in Healthcare

Sat, 14 May 2022 08:23:24 +0000

Author Names: Spector-Bagdady K.,Rahimzadeh V.,Jaffe K.,Moreno J.

Database Source: Embase Weekly Updates

Journal Title: The American journal of bioethics : AJOB

Article Title: Promoting Ethical Deployment of Artificial Intelligence and Machine Learning in Healthcare

Year: 2022

Issue: 5

Volume: 22

Abstract:

Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning

Mon, 02 May 2022 00:00:00 +0000

J Law Biosci. 2022 Apr 28;9(1):lsac012. doi: 10.1093/jlb/lsac012. eCollection 2022 Jan-Jun.

ABSTRACT

The distribution of crucial medical goods and services in conditions of scarcity is among the most important, albeit contested, areas of public policy development. Policymakers must strike a balance between multiple efficiency and fairness objectives, while reconciling disparate value judgments from a diverse set of stakeholders. We present a general framework for combining ethical theory, data modeling, and stakeholder input in this process and illustrate through a case study on designing organ transplant allocation policies. We develop a novel analytical tool, based on machine learning and optimization, designed to facilitate efficient and wide-ranging exploration of policy outcomes across multiple objectives. Such a tool enables all stakeholders, regardless of their technical expertise, to more effectively engage in the policymaking process by developing evidence-based value judgments based on relevant tradeoffs.

PMID:35496981 | PMC:PMC9050238 | DOI:10.1093/jlb/lsac012