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Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies

The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all...

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Detalles Bibliográficos
Autores principales: Ott, Tabea, Dabrock, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444183/
https://www.ncbi.nlm.nih.gov/pubmed/36072654
http://dx.doi.org/10.3389/fgene.2022.902960
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author Ott, Tabea
Dabrock, Peter
author_facet Ott, Tabea
Dabrock, Peter
author_sort Ott, Tabea
collection PubMed
description The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all areas of life and its increasing invisibility and opacity, new questions of transparency arise. While the complex human-machine interactions involved in deploying and using AI tend to become non-transparent, the use of these technologies makes the patient seemingly transparent. Papers on the ethical implementation of AI plead for transparency but neglect the factor of the “transparent patient” as intertwined with AI. Transparency in this regard appears to be Janus-faced: The precondition for receiving help - e.g., treatment advice regarding the own health - is to become transparent for the digitized health care system. That is, for instance, to donate data and become visible to the AI and its operators. The paper reflects on this entanglement of transparent patients and (non-) transparent technology. It argues that transparency regarding both AI and humans is not an ethical principle per se but an infraethical concept. Further, it is no sufficient basis for avoiding harm and human dignity violations. Rather, transparency must be enriched by intelligibility following Judith Butler’s use of the term. Intelligibility is understood as an epistemological presupposition for recognition and the ensuing humane treatment. Finally, the paper highlights ways to testify intelligibility in dealing with AI in health care ex ante, ex post, and continuously.
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spelling pubmed-94441832022-09-06 Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies Ott, Tabea Dabrock, Peter Front Genet Genetics The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all areas of life and its increasing invisibility and opacity, new questions of transparency arise. While the complex human-machine interactions involved in deploying and using AI tend to become non-transparent, the use of these technologies makes the patient seemingly transparent. Papers on the ethical implementation of AI plead for transparency but neglect the factor of the “transparent patient” as intertwined with AI. Transparency in this regard appears to be Janus-faced: The precondition for receiving help - e.g., treatment advice regarding the own health - is to become transparent for the digitized health care system. That is, for instance, to donate data and become visible to the AI and its operators. The paper reflects on this entanglement of transparent patients and (non-) transparent technology. It argues that transparency regarding both AI and humans is not an ethical principle per se but an infraethical concept. Further, it is no sufficient basis for avoiding harm and human dignity violations. Rather, transparency must be enriched by intelligibility following Judith Butler’s use of the term. Intelligibility is understood as an epistemological presupposition for recognition and the ensuing humane treatment. Finally, the paper highlights ways to testify intelligibility in dealing with AI in health care ex ante, ex post, and continuously. Frontiers Media S.A. 2022-08-22 /pmc/articles/PMC9444183/ /pubmed/36072654 http://dx.doi.org/10.3389/fgene.2022.902960 Text en Copyright © 2022 Ott and Dabrock. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Ott, Tabea
Dabrock, Peter
Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies
title Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies
title_full Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies
title_fullStr Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies
title_full_unstemmed Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies
title_short Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies
title_sort transparent human – (non-) transparent technology? the janus-faced call for transparency in ai-based health care technologies
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444183/
https://www.ncbi.nlm.nih.gov/pubmed/36072654
http://dx.doi.org/10.3389/fgene.2022.902960
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