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      It’s Just a Recipe?—Comparing Expert and Lay User Understanding of Algorithmic Systems

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          Abstract

          Algorithmic systems may appear opaque to users. This can hinder users from making informed decisions about the use of such systems. To combat this, explanations intend to make them more transparent. However, explanations are typically informed by the system properties. We argue that they also need to consider the user understanding in order to be more comprehensible to users. To achieve these user-informed explanations, this qualitative work aims to (a) compare how experts and lay users understand algorithmic systems and (b) derive implications for creating user-informed explanations. We conducted an expert focus group ( N = 3) and semistructured in-depth interviews with experts ( N = 10) and lay users ( N = 11), including a drawing task. Reflexive thematic analysis by the first author revealed group-specific and common themes: Experts understood algorithms as a decision-making process and were aware of the context dependency of algorithms. Lay users, in turn, understood algorithms as intelligence and as data structuring. They focused on the tangible and visible elements of algorithmic systems. Both groups also understood algorithms as a sequence of actions. The different understandings might be driven by group-specific experiences and purposes to use algorithmic systems. Based on our results, we argue that user-informed explanations could consider the context dependency of algorithmic systems and highlight their limitations.

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          Using thematic analysis in psychology

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            Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study.

            Qualitative content analysis and thematic analysis are two commonly used approaches in data analysis of nursing research, but boundaries between the two have not been clearly specified. In other words, they are being used interchangeably and it seems difficult for the researcher to choose between them. In this respect, this paper describes and discusses the boundaries between qualitative content analysis and thematic analysis and presents implications to improve the consistency between the purpose of related studies and the method of data analyses. This is a discussion paper, comprising an analytical overview and discussion of the definitions, aims, philosophical background, data gathering, and analysis of content analysis and thematic analysis, and addressing their methodological subtleties. It is concluded that in spite of many similarities between the approaches, including cutting across data and searching for patterns and themes, their main difference lies in the opportunity for quantification of data. It means that measuring the frequency of different categories and themes is possible in content analysis with caution as a proxy for significance. © 2013 Wiley Publishing Asia Pty Ltd.
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              One size fits all? What counts as quality practice in (reflexive) thematic analysis?

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                Author and article information

                Journal
                Technology, Mind, and Behavior
                American Psychological Association
                2689-0208
                November 8, 2021
                : 2
                : 4
                Affiliations
                [1]Department of Computer Science and Applied Cognitive Science, Research Training Group “User-Centred Social Media,” University of Duisburg-Essen
                [2]Department of Computer Science and Applied Cognitive Science, Social Psychology: Media and Communication, University of Duisburg-Essen
                Author notes
                Action Editor: Danielle S. McNamara was the action editor for this article.
                Acknowledgment: This work was supported by the Deutsche Forschungsgemeinschaft (DFG) under Grant No. GRK 2167, Research Training Group “User-Centred Social Media.” We acknowledge support by the Open Access Publication Fund of the University of Duisburg-Essen.
                Conflict of interest: The authors declare that there is no conflict of interest.
                Data availability statement: Interview transcripts are not publicly available due to them containing information that could compromise research participant privacy and informed consent. Use case descriptions, interview guidelines, and codes that support the findings of this study are openly available on the Open Science Framework (OSF): https://osf.io/72tgn/.
                [*] Thao Ngo, Department of Computer Science and Applied Cognitive Science, Research Training Group “User-Centred Social Media,” University of Duisburg-Essen, Forsthausweg 2, 47057 Duisburg, Germany thao.ngo@uni-due.de
                Author information
                https://orcid.org/0000-0001-5147-8272
                https://orcid.org/0000-0001-7535-870X
                Article
                2022-01159-001
                10.1037/tmb0000045
                0a07b084-b243-40a5-b0a3-a084b2ee8a8e
                © 2021 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

                History

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                thematic analysis,explanation,transparency,drawing task,qualitative study

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