The de-legitimation of Machine Learning Algorithms (MLAs) in “The Social Dilemma” (2020): a post-digital cognitive-stylistic approach

IF 2 Q1 LINGUISTICS
Nashwa Elyamany
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Abstract

Released on Netflix, the most popular algorithm-oriented streaming service, The Social Dilemma (TSD) is a vivid manifestation of how the recent advancements in Artificial Intelligence and Machine Learning Algorithms (MLAs) have turned both to new species of post-digital, semio-cognitive power. Premised on the conception of MLAs as non-human intermediaries, this research endeavor proposes a novel post-digital ethnography of technologically-mediated algorithmic contexts and takes the challenge of examining MLAs as distributed, contested, and unbounded figures in the filmic narrative of this Netflix production. For the purpose, the paper employs post-digital cognitive-stylistic analytical tools, geared by van Leeuwen’s (de)-legitimation strategies, to showcase how MLAs, as socio-technical actors, are semio-cognitively materialized through spatio-temporal, narrative-immersive de-legitimating patterns. The examination of algorithms as socio-technical imaginary agents fully integrated within sociotechnical assemblages yields insightful findings. Delving deep into the multiple “posts” in the post-digital milieu of the film, the analysis affords valuable results that reframe, rename, and de-legitimate MLAs’ performative agency that is not only procedural-computational, but is socio-technical, semio-discursive, and cognitive-stylistic as well.
社会困境》(2020)中机器学习算法(MLA)的去合法化:一种后数字认知风格的方法
社交困境》(The Social Dilemma,简称 TSD)在最受欢迎的算法流媒体服务 Netflix 上映,生动展示了人工智能和机器学习算法(MLAs)的最新进展如何将二者转化为后数字、半认知力量的新物种。本研究以作为非人类中介的工作重点这一概念为前提,对以技术为媒介的算法语境提出了一种新的后数字人种学研究方法,并将工作重点作为 Netflix 出品的这部电影叙事中的分布式、有争议和无约束的人物形象进行研究。为此,本文采用了后数字认知风格分析工具,以 van Leeuwen 的(去)合法化策略为导向,展示了作为社会技术行为者的工作重点是如何通过时空、叙事浸入式的去合法化模式,在半认知状态下具体化的。算法作为社会技术的想象主体,完全融入了社会技术的组合之中,对算法的研究产生了富有洞察力的发现。深入研究影片后数字环境中的多个 "职位",分析提供了有价值的结果,对工作重点的表演性代理进行了重构、重新命名和去合法化,这种代理不仅是程序性的计算代理,也是社会技术性的、半话语性的和认知风格性的代理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.00
自引率
80.00%
发文量
10
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