Trust, but verify: informed consent, AI technologies, and public health emergencies
Trust, but verify: informed consent, AI technologies, and public health emergencies
To use technology or engage with research or medical treatment typically requires user consent: agreeing to terms of use with technology or services, or providing informed consent for research participation, for clinical trials and medical intervention, or as one legal basis for processing personal data. Introducing AI technologies, where explainability and trustworthiness are focus items for both government guidelines and responsible technologists, imposes additional challenges. Understanding enough of the technology to be able to make an informed decision, or consent, is essential but involves an acceptance of uncertain outcomes. Further, the contribution of AIenabled technologies not least during the COVID-19 pandemic raises ethical concerns about the governance associated with their development and deployment. Using three typical scenarios— contact tracing, big data analytics and research during public emergencies—this paper explores a trustbased alternative to consent. Unlike existing consent-based mechanisms, this approach sees consent as a typical behavioural response to perceived contextual characteristics. Decisions to engage derive from the assumption that all relevant stakeholders including research participants will negotiate on an ongoing basis. Accepting dynamic negotiation between the main stakeholders as proposed here introduces a specifically socio–psychological perspective into the debate about human responses to artificial intelligence. This trust-based consent process leads to a set of recommendations for the ethical use of advanced technologies as well as for the ethical review of applied research projects.
AI-Technologies, Big data, COVID-19, Informed Consent, Public Health Emergency, Research ethics, Technology acceptance, Terms of Use, Trust, contact tracing
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
May 2021
Pickering, Brian
225088d0-729e-4f17-afe2-1ad1193ccae6
Pickering, Brian
(2021)
Trust, but verify: informed consent, AI technologies, and public health emergencies.
Future Internet, 13 (5), [132].
(doi:10.3390/fi13050132).
Abstract
To use technology or engage with research or medical treatment typically requires user consent: agreeing to terms of use with technology or services, or providing informed consent for research participation, for clinical trials and medical intervention, or as one legal basis for processing personal data. Introducing AI technologies, where explainability and trustworthiness are focus items for both government guidelines and responsible technologists, imposes additional challenges. Understanding enough of the technology to be able to make an informed decision, or consent, is essential but involves an acceptance of uncertain outcomes. Further, the contribution of AIenabled technologies not least during the COVID-19 pandemic raises ethical concerns about the governance associated with their development and deployment. Using three typical scenarios— contact tracing, big data analytics and research during public emergencies—this paper explores a trustbased alternative to consent. Unlike existing consent-based mechanisms, this approach sees consent as a typical behavioural response to perceived contextual characteristics. Decisions to engage derive from the assumption that all relevant stakeholders including research participants will negotiate on an ongoing basis. Accepting dynamic negotiation between the main stakeholders as proposed here introduces a specifically socio–psychological perspective into the debate about human responses to artificial intelligence. This trust-based consent process leads to a set of recommendations for the ethical use of advanced technologies as well as for the ethical review of applied research projects.
Text
futureinternet-13-00132
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More information
Accepted/In Press date: 12 May 2021
e-pub ahead of print date: 18 May 2021
Published date: May 2021
Additional Information:
Funding Information:
Funding: This work was funded in part by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780495 (project BigMedilytics). Disclaimer: Any dissemination of results here presented reflects only the author’s view. The Commission is not responsible for any use that may be made of the information it contains. It was also supported, in part, by the Bill & Melinda Gates Foundation [INV-001309]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.
Publisher Copyright:
© 2021 by the author. Licensee MDPI, Basel, Switzerland.
Keywords:
AI-Technologies, Big data, COVID-19, Informed Consent, Public Health Emergency, Research ethics, Technology acceptance, Terms of Use, Trust, contact tracing
Identifiers
Local EPrints ID: 449331
URI: http://eprints.soton.ac.uk/id/eprint/449331
ISSN: 1999-5903
PURE UUID: a1d2057c-362c-4ae0-98dd-05c8b1cdc103
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Date deposited: 25 May 2021 16:31
Last modified: 17 Mar 2024 03:23
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