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AI, big data, and the future of consent
In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operational...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404542/ https://www.ncbi.nlm.nih.gov/pubmed/34483498 http://dx.doi.org/10.1007/s00146-021-01262-5 |
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author | Andreotta, Adam J. Kirkham, Nin Rizzi, Marco |
author_facet | Andreotta, Adam J. Kirkham, Nin Rizzi, Marco |
author_sort | Andreotta, Adam J. |
collection | PubMed |
description | In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. |
format | Online Article Text |
id | pubmed-8404542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-84045422021-08-30 AI, big data, and the future of consent Andreotta, Adam J. Kirkham, Nin Rizzi, Marco AI Soc Open Forum In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. Springer London 2021-08-30 2022 /pmc/articles/PMC8404542/ /pubmed/34483498 http://dx.doi.org/10.1007/s00146-021-01262-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Open Forum Andreotta, Adam J. Kirkham, Nin Rizzi, Marco AI, big data, and the future of consent |
title | AI, big data, and the future of consent |
title_full | AI, big data, and the future of consent |
title_fullStr | AI, big data, and the future of consent |
title_full_unstemmed | AI, big data, and the future of consent |
title_short | AI, big data, and the future of consent |
title_sort | ai, big data, and the future of consent |
topic | Open Forum |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404542/ https://www.ncbi.nlm.nih.gov/pubmed/34483498 http://dx.doi.org/10.1007/s00146-021-01262-5 |
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