2020 marks the 25th anniversary of the “digital divide.” Although a quarter century has passed, legacy digital inequalities continue, and emergent digital inequalities are proliferating. Many of the initial schisms identified in 1995 are still relevant today. Twenty-five years later, foundational access inequalities continue to separate the digital haves and the digital have-nots within and across countries. In addition, even ubiquitous-access populations are riven with skill inequalities and differentiated usage. Indeed, legacy digital inequalities persist vis-à-vis economic class, gender, sexuality, race and ethnicity, aging, disability, healthcare, education, rural residency, networks, and global geographies. At the same time, emergent forms of inequality now appear alongside legacy inequalities such that notions of digital inequalities must be continually expanded to become more nuanced. We capture the increasingly complex and interrelated nature of digital inequalities by introducing the concept of the “digital inequality stack.” The concept of the digital inequality stack encompasses access to connectivity networks, devices, and software, as well as collective access to network infrastructure. Other layers of the digital inequality stack include differentiated use and consumption, literacies and skills, production and programming, etc. When inequality exists at foundational layers of the digital inequality stack, this often translates into inequalities at higher levels. As we show across these many thematic foci, layers in the digital inequality stack may move in tandem with one another such that all layers of the digital inequality stack reinforce disadvantage.
This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.
This chapter focuses on the role of digital intermediaries in shaping technology, society, and economy under what Casilli and Posada call “the paradigm of the platform.” They trace the historical relationship between platforms, markets, and enterprises to demonstrate the role of algorithms in matching users, pieces of software, goods, and services, and how platforms can create value from the content and data generated by users. Their primary argument is that platforms play a fundamental role in establishing a digital labor relationship with their users by allocating underpaid or unpaid tasks to them. In order to enable and coordinate users’ contributions, platforms need to standardize and fragment (“taskify”) labor processes. The authors conclude by highlighting the link between platformization and automation, with the tech giants employing their users’ data to produce artificial intelligence and machine-learning solutions to an expanding range of problems.
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