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      The hidden assumptions in public engagement: A case study of engaging on ethics in government data analysis

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          Abstract

          This study examines the hidden assumptions around running public-engagement exercises in government. We study an example of public engagement on the ethics of combining and analysing data in national government – often called data science ethics. We study hidden assumptions, drawing on hidden curriculum theories in education research, as it allows us to identify conscious and unconscious underlying processes related to conducting public engagement that may impact results. Through participation in the 2016 Public Dialogue for Data Science Ethics in the UK, four key themes were identified that exposed underlying publicengagement norms. First, that organizers had constructed a strong imagined public as neither overly critical nor supportive, which they used to find and engage participants. Second, that official aims of the engagement, such as including publics in developing ethical data regulations, were overshadowed by underlying meta-objectives, such as counteracting public fears. Third, that advisory group members, organizers and publics understood the term 'engagement' in varying ways, from creating interest to public inclusion. And finally, that stakeholder interests, particularly government hopes for a positive report, influenced what was written in the final report. Reflection on these underlying mechanisms, such as the development of meta-objectives that seek to benefit government and technical stakeholders rather than publics, suggests that the practice of public engagement can, in fact, shut down opportunities for meaningful public dialogue.

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          Journal
          72010652
          Research for All
          UCL IOE Press
          2399-8121
          12 September 2019
          : 3
          : 2
          : 180-190
          Article
          2399-8121(20190912)3:2L.180;1- s5.phd /ioep/rfa/2019/00000003/00000002/art00005
          10.18546/RFA.03.2.05
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          Research for All
          Volume 3, Issue 2

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