"Shadowbanning is not a thing": black box gaslighting and the power to independently know and credibly critique algorithms
"Shadowbanning is not a thing": black box gaslighting and the power to independently know and credibly critique algorithms
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DOI：
10.1080/1369118x.2021.1994624
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发表时间：
2021-10-27
影响因子：
4.2
通讯作者：
Cotter, Kelley
中科院分区：
文献类型：
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作者：
Cotter, Kelley
治理算法的努力集中在“黑箱问题”上，即由于公司保密和技术复杂性而导致的算法不透明。在本文中，我将概念化一个与治理工作相关的、同样基本的挑战：黑盒照明。黑箱气体照明捕捉到平台如何利用其对算法的认知权威来破坏用户对算法的信心，并破坏可信的批评。我通过对Instagram影响者社区内的“禁止阴影”争议的案例研究，利用对影响者的采访（n = 17）和在线话语材料（例如，社交媒体帖子、博客帖子、视频等）。我认为，黑匣子煤气灯为那些寻求问责的人提供了一个强大的威慑：一场关于批评合法性的知识竞赛，其中平台占据上风。与此同时，我建议我们必须注意平台对“真相”的片面性，以及用户对算法的理解的价值。
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.