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 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?.
| Errataetall: |
CommentIn: Am J Bioeth. 2022 Jul;22(7):26-28. - PMID 35737486 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
The American journal of bioethics : AJOB - 22(2022), 7 vom: 21. Juli, Seite 4-20 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Meier, Lukas J [VerfasserIn] |
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| Links: |
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| Themen: |
Algorithms |
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| Anmerkungen: |
Date Completed 27.06.2022 Date Revised 07.10.2022 published: Print-Electronic CommentIn: Am J Bioeth. 2022 Jul;22(7):26-28. - PMID 35737486 Citation Status MEDLINE |
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| doi: |
10.1080/15265161.2022.2040647 |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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? | ||
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