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We live in a digital world where rapidly evolving technology dramatically impacts daily life, and our digital actions and other online activities are continually observed, transforming us into digital data subjects. Thus, digital sociology ought to explore the consequences of digital technologies, their incorporation into social institutions, and their impact on the exclusion of vulnerable populations. The digital divide literature fails to distinguish between digital equality and digital equity; instead it mainly focuses on disparities in access, skills, and outcomes. The availability of unified or standardized digital platforms, which I refer to as 'white technologies,' for all segments of society, as well as equal access to digital services, does not guarantee digital equity, as underserved groups and culturally diverse communities may have distinct needs and pathways to overcome the third digital divide related to comparable 'capital-enhancing' outcomes.
Using a sociomaterial approach and a conceptualization of the affordances of digital unified platforms, this article will explore growing digital inequities, notably among underserved and culturally diverse communities that oppose the monopolistic power generated by 'white technologies'. The preference for dominant corporate platforms, principles, and designs that use data and user networks to gain monopolistic advantages without government oversight diverts attention away from alternative co-designed participatory digital platforms that are not profitable, can resolve ambiguity and contradiction, and align with diverse groups' cultural needs. Preferred social (cultural) determinants of digital platforms, combined with limited access to digital technology, put disadvantaged individuals at risk of digital marginalization.
The paper proposes addressing the digital equity gap and questions who is responsible for doing so, given that the Internet is dominated by a market-oriented system that favors profitable platforms and their affordances, as well as cost-effective algorithmic measures.