Abstract
In this paper, I will investigate the possible impact of weak artificial intelligence (more specifically, I will concentrate on deep learning) on human capability of action. For this goal, I will first address Arendt’s philosophy of action, which seeks to emphasize the distinguishing elements of action that set it apart from other forms of human activity. According to Arendt, action should be conceived as praxis, an activity that has its goal in its own very performance. The authentic meaning of action includes the “passion” for articulation of one’s own individuality; I can only manifest myself as a distinct personality insofar as I introduce myself as a novel beginning to the web of human interactions demonstrating both my relevance and distinction from others. From this Arendtian standpoint, I will analyse the impact of deep learning in modern AI from two possible angles. First, I will argue that the direct interaction between AI and action is impossible. Since AI operates on the principle of efficiency, it can neither suggest certain goals for action for us nor overtake their implementation because action is not guided by the instrumental need to be efficient but by the existential desire to be someone. Second, I will also analyse the possibility of the indirect impact of AI on action. More specifically, I analyse neural network’s ability to circulate actions among individuals based on mathematical calculation. As I will argue, the efficiency of this circulation that surpasses human cognitive capacities can potentially organize a broader network of interaction among individuals and serve as a catalyst for the ability to act.
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Notes
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In what follows, I will largely concentrate on one particular case of how DNNs mobilize human action taking for example Russian protest movement in 2018. However, I am convinced that this account has wider applicability. For example, the protest movement in Iran in 2022 demonstrated similar patterns when the feministic movement “Hair for Freedom” virally spread in TikTok and Instagram involving not only teenage females but also various groups of males into the protests (see Navarro & Peres-Neto, 2023; Afary & Anderson, 2023). See also Bossetta (2018) for analysing how DNNs algorithms in Snapchat, Instagram etc. have affected the 2016 US selection.
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One concern that might arise in this context is that by connecting people to one another this way, DNNs might not only facilitate human plurality but also endanger it; by bringing individuals together based on statistical likeness, they might as well give voice to fascist or racist people. To avoid this worry, we should recall the inherent riskiness of Arendt’s notion of action that doesn’t want any safeguards against ‘wrong’ people entering the public domain but rather relies on its power to counter-act and gather others in this counter-action. Action does not seek to secure any particular regime but throws itself into the open increasing the turbulency of our being-with-one-another being prepared to encounter hostile others as well.
The discussion of the possible impact of DNNs on action should be distinguished from parallel usages of Arendt that see.
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Acknowledgements
The publication was prepared as part of the “Competition for 2024–2026 Postdoctoral Job Positions at the University of Hradec Králové”, at the Philosophical Faculty, University of Hradec Králové. I want to thank Algirdas Tiuninas for commenting on the drafts of this paper and Martin Braun for sharing his work with me.
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The author has received funding from the University of Hradec Kralove.
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Koloskov, D. AI-informed acting: an Arendtian perspective. Phenom Cogn Sci (2024). https://doi.org/10.1007/s11097-024-09962-1
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DOI: https://doi.org/10.1007/s11097-024-09962-1