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Seeing like a Driver/Seeing like a Platform: Comparing Algorithmic and Experiential Visions of the City

Rida Qadri
Catherine D'Ignazio
Big Data & Society (2023)
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Abstract

In this paper we interrogate the relationship between two different ways of seeing and knowing urban mobility markets: a top-down algorithmic vision of mobility platforms and a bottom-up experiential vision of drivers. By juxtaposing both perspectives, we argue that these visions do not exist in binaries but in a complex dance of complementarity and competition. The paper dissects two assumptions that the Platform’s View from Above makes in the context of Jakarta: 1) Urban space is an orderable, knowable and abstract container of supply and demand; 2) Drivers are optimizable, interchangeable dots on a map. For each assumption of the platform’s View from Above, we show how the drivers’ experience these assumptions and how their View from Within responds to the gaps in the former. We argue that the Driver's View from Within doesn't only act as a form of resistance to the platform but also as a mode of survival, acquiescence, subversion and encroachment. We thus reflect on the opportunities this entanglement presents for worker agency and any hopes for more ‘worker centered design’ in platform economies. We conclude with thoughts on the power and value of alternative forms of optimizations in our cities.