A theoretical and policy approach to platform regulation
Digital platforms used to be spaces where consumers interacted with advertisers within a complex web of data relations (Gillespie, 2018a). But their trajectory has now moved beyond the domain of economy and have emerged as de facto infrastructures for social life—from the idea of ‘Platform Capitalism’ (Srnicek, 2017) to ‘Platform Society’ (Van Dijck et al., 2018), which explains how deeply platforms are embedded in social, economic, cultural, and legal institutions and practices (Van Dijck et al., 2018, p. 2).
If digital platform is understood as a ‘social figuration’ (Couldry & Hepp, 2017; Elias, 1982), this paper discerns two core processes within it which have immense ordering power: datafication and personalization (see Figure 1).
Datafication consists of massive user surveillance (Zuboff, 2019) and categorization (Gandy, 1993; Kitchin, 2014) of the collected data and is more oriented to knowledge. Personalization is comprised of ever-modulating predictions based on that data and of a hyper fragmentation of users, consumers, or citizens to apply those predictions on; it is more inclined toward practice.
Predictive analytics is now widely used in numerous areas such as risk assessments (such as insurance, policing, health, banking, credit and loans), recommender systems (such as e-commerce, and entertainment), and hypernudging (such as advertising and political campaigning). The more data are fed into predictive engines, the more accurate these predictions become; therefore, platforms are best places for hosting these engines.
Fragmentation is the automated process of segmenting the audiences into the smallest chunks so as to serve them with tailored suggestions or messages, services, and products.
The potential implications of mass personalization and its two sub-processes raise serious ethical concerns, from erosion of autonomy and solidarity to threats against democracy, and social justice (Yeung, 2018). When social action can be accurately predicted on an individual level, various actors may be able to nudge (Thaler & Sunstein, 2008) or influence users more effectively toward certain actions with commercial or political aims (Mills, 2022; Sunstein, 2012, 2013). This may undermine the citizen autonomy as the foundation of participant democracy (Leggett, 2014; Yeung, 2017, 2018).
Such concerns have incited global debates about the regulations of platforms and their embedded artificial intelligence models. Based on the proposed conceptual model, this paper proposes a regulatory approach.
Platform neutrality is the proposed term for a legal and technical unbundling of the platform’s core code, its algorithms (or AI models), and the user data which will enable users to utilise third-party algorithms on all platforms. In other words, making platforms neutral to the algorithms and AI models they use. This will instigate a free market of algorithms where concerns about monopoly, transparency, accountability, and privacy will be resolved through competition.
Figure 2. Platform layers
Similar approach had been previously devised in 1990s where the unbundling of computer hardware, Operating System, and software completely changed personal computing around the world. In the platform era, the core code is the equivalent of hardware, the algorithms the OS, and the user data resembles the software.
Some examples may be useful: A private company could sell alternative navigation algorithms, to be used as plugins on Google or Apple Maps, which suggests routes with lower emission, or routes that help local shops, or those that are safer at night or need to become safer by more traffic, etc.
Another company could provide newsfeed algorithms for Facebook or Instagram which, not only explain what kind of content they prioritize, but allow users to customize their own or their children’s newsfeeds.
Platform neutrality is a creative way to use neoliberal market dynamics against itself for the public good.
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