Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.
Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress' prima-facie principles could be employed to advise on a range o...
| Publicado en: | American Journal of Bioethics Vol. 22; no. 7; pp. 4 - 21 |
|---|---|
| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157638692&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157638692 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15265161 FKZ jtl: American Journal of Bioethics issn: 15265161 maglogo: N pubinfo: dt: Jul2022 vid: 22 iid: 7 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 157638692 155775241 157638692 157638692 10.1080/15265161.2022.2040647 157638692 ppf: 4 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept. aug: au: Meier, Lukas J. Hein, Alice Diepold, Klaus Buyx, Alena affil: Technical University of Munich sug: subj: Algorithms Bioethics Decision Making, Ethical Health Facilities Human Artificial Intelligence Machine Learning Beneficence Medical Staff Task Performance and Analysis Patient Autonomy ab: Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress' prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|