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      Trading off accuracy and explainability in AI decision-making: findings from 2 citizens’ juries

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          Abstract

          Objective

          To investigate how the general public trades off explainability versus accuracy of artificial intelligence (AI) systems and whether this differs between healthcare and non-healthcare scenarios.

          Materials and Methods

          Citizens’ juries are a form of deliberative democracy eliciting informed judgment from a representative sample of the general public around policy questions. We organized two 5-day citizens’ juries in the UK with 18 jurors each. Jurors considered 3 AI systems with different levels of accuracy and explainability in 2 healthcare and 2 non-healthcare scenarios. Per scenario, jurors voted for their preferred system; votes were analyzed descriptively. Qualitative data on considerations behind their preferences included transcribed audio-recordings of plenary sessions, observational field notes, outputs from small group work and free-text comments accompanying jurors’ votes; qualitative data were analyzed thematically by scenario, per and across AI systems.

          Results

          In healthcare scenarios, jurors favored accuracy over explainability, whereas in non-healthcare contexts they either valued explainability equally to, or more than, accuracy. Jurors’ considerations in favor of accuracy regarded the impact of decisions on individuals and society, and the potential to increase efficiency of services. Reasons for emphasizing explainability included increased opportunities for individuals and society to learn and improve future prospects and enhanced ability for humans to identify and resolve system biases.

          Conclusion

          Citizens may value explainability of AI systems in healthcare less than in non-healthcare domains and less than often assumed by professionals, especially when weighed against system accuracy. The public should therefore be actively consulted when developing policy on AI explainability.

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          Most cited references24

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          The Discovery of Grounded Theory

          <p>Most writing on sociological method has been concerned with how accurate facts can be obtained and how theory can thereby be more rigorously tested. In The Discovery of Grounded Theory, Barney Glaser and Anselm Strauss address the equally Important enterprise of how the discovery of theory from data--systematically obtained and analyzed in social research--can be furthered. The discovery of theory from data--grounded theory--is a major task confronting sociology, for such a theory fits empirical situations, and is understandable to sociologists and laymen alike. Most important, it provides relevant predictions, explanations, interpretations, and applications.</p><p>In Part I of the book, Generation Theory by Comparative Analysis, the authors present a strategy whereby sociologists can facilitate the discovery of grounded theory, both substantive and formal. This strategy involves the systematic choice and study of several comparison groups. In Part II, The Flexible Use of Data, the generation of theory from qualitative, especially documentary, and quantitative data Is considered. In Part III, Implications of Grounded Theory, Glaser and Strauss examine the credibility of grounded theory.</p><p>The Discovery of Grounded Theory is directed toward improving social scientists' capacity for generating theory that will be relevant to their research. While aimed primarily at sociologists, it will be useful to anyone Interested In studying social phenomena--political, educational, economic, industrial-- especially If their studies are based on qualitative data.</p></p>
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            Machine learning: Trends, perspectives, and prospects.

            Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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              The global landscape of AI ethics guidelines

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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                October 2021
                01 August 2021
                01 August 2021
                : 28
                : 10
                : 2128-2138
                Affiliations
                [1 ]Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester , Manchester, UK
                [2 ]NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester , Manchester, UK
                [3 ]Division of Pharmacy and Optometry, School of Health Sciences, The University of Manchester , Manchester, UK
                [4 ]Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, The University of Manchester , Manchester, UK
                [5 ]Jefferson Center , Saint Paul, Minnesota, USA
                [6 ]Information Commissioner’s Office , Wilmslow, UK
                [7 ]School of Law, Faculty of Humanities, The University of Manchester , Manchester, UK
                [8 ]Citizens’ Juries CIC , Manchester, UK
                Author notes
                [*]

                Authors contributed equally.

                Corresponding Author: Niels Peek, MSc, PhD, Centre for Health Informatics, University of Manchester, Vaughan House, Portsmouth Street, Manchester, United Kingdom, M13 9GB, Email: niels.peek@ 123456manchester.ac.uk Phone number: 044 161 3060674
                Author information
                https://orcid.org/0000-0003-0929-436X
                https://orcid.org/0000-0002-6393-9969
                Article
                ocab127
                10.1093/jamia/ocab127
                8522832
                34333646
                db086ab4-c7bf-4c5e-a100-a45fd4a7a2a1
                © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 09 March 2021
                : 02 June 2021
                : 05 June 2021
                : 16 September 2021
                Page count
                Pages: 11
                Funding
                Funded by: National Institute for Health Research Greater Manchester Patient Safety Translational Research Centre (NIHR GM PSTRC);
                Funded by: Information Commissioner’s Office;
                Categories
                Research and Applications
                AcademicSubjects/MED00580
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01530
                AcademicSubjects/MED00580
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01530

                Bioinformatics & Computational biology
                artificial intelligence,choice behavior/ethics,citizens’ jury, public opinion,qualitative research

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