AI in the Everyday: Machine Learning’s Role in Reshaping Digital Culture and Practices
Keywords:
Algorithmic governance, Algorithmic mediation, Artificial Intelligence, Datafication, Digital culture, Digital identity, Everyday Practices, Machine Learning, Recommendation Systems, social mediaAbstract
This article examines how embedded Machine Learning (ML) systems are reshaping everyday digital experiences, including social media use, smart-enabled devices, and recommendation-driven consumption. Drawing on recent empirical studies and policy reports, the article synthesizes secondary evidence indicating the rapid expansion of generative AI and large language model–based platforms, with current global estimates suggesting over one billion monthly active users. At the infrastructural level, industry analyses consistently report that a substantial proportion of connected devices now incorporate some form of ML functionality, highlighting the growing pervasiveness of AI in routine digital life. Social media platforms, serving approximately 5.66 billion global user accounts, are increasingly organized around algorithmic curation, personalization, and automated content moderation, reshaping patterns of visibility, attention, and engagement within digital cultures. Building on interdisciplinary research, this review examines how opaque algorithmic decision-making influences user perceptions, creative practices, and social interaction, while simultaneously transforming expectations related to convenience, privacy, and trust. Secondary survey evidence further points to a contextual divide in public attitudes toward AI technologies. While a majority of adults in the United States report comfort with AI-based recommendation systems, acceptance declines notably when similar technologies are associated with profiling, facial recognition, or surveillance-oriented applications. Overall, the reviewed literature suggests that artificial intelligence is not merely augmenting existing digital cultures, but is actively reconfiguring social practices, power relations, and cultural imaginaries within data-driven environments.
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