“Shadowbanning is not a thing”: black box gaslighting and the power to independently know and credibly critique algorithms

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37 Scopus citations

Abstract

Efforts to govern algorithms have centerd the ‘black box problem,’ or the opacity of algorithms resulting from corporate secrecy and technical complexity. In this article, I conceptualize a related and equally fundamental challenge for governance efforts: black box gaslighting. Black box gaslighting captures how platforms may leverage perceptions of their epistemic authority on their algorithms to undermine users’ confidence in what they know about algorithms and destabilize credible criticism. I explicate the concept of black box gaslighting through a case study of the ‘shadowbanning’ dispute within the Instagram influencer community, drawing on interviews with influencers (n = 17) and online discourse materials (e.g., social media posts, blog posts, videos, etc.). I argue that black box gaslighting presents a formidable deterrent for those seeking accountability: an epistemic contest over the legitimacy of critiques in which platforms hold the upper hand. At the same time, I suggest we must be mindful of the partial nature of platforms’ claim to ‘the truth,’ as well as the value of user understandings of algorithms.

Original languageEnglish (US)
JournalInformation Communication and Society
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Communication
  • Library and Information Sciences

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