Can Social Media be Sustainable: Economic and Industrial Modelling Instruments to Mitigate the Unneeded use of Resources in Social Media and Artificial Intelligence

Abstract

Social media can help businesses and society implement sustainable practices (Bodin & Prell (Eds.), 2011). They also contribute to excessive resource use in data collection and sharing, digital resource use, and energy consumption (Kamin & Paireekreng, 2018). Thus, TikTok's feeds consume 15.81 mAh per minute and emit 2.63 gEqCO2/min in 2021. (Derudder, 2021). The generation that uses this tool most (Burns-Stanning, 2020) is also the most demanding and aware of climate change issues (Knight, 2016), even though its practices contribute to the current degradation. This hiatus illustrates how difficult it is to reconcile individual and collective goals. We propose shifting the question from the end-user to the service provider by detecting sobriety pits in data use and access to perpetuate or avoid disrupting end-user practices. Sobriety (frugality) is a critical variable in energy transition scenarios (Balzani, 2019) and one of the ecological transition's pillars. Social media and AI are particularly tense on these issues (Stein, 2020). Current solutions often pit energy efficiency against performance (Ikhlasse, Benjamin, Vincent & Hicham, 2021). Given the industry's promises of information access, automation, intelligent decision-making, human error avoidance, and more, this is unacceptable. Thus, we propose an operational framework to pose a model of sobriety for data access, consumption, and use (such as in social media or AI agents training) that does not impair performance or accuracy.