Emancipating Gig Workers from Algorithmic Control: A Multi-disciplinary Inquiry into Data Rights, Data Flows and Data-based Worker Science - Karen Gregory
From James Stewart on March 31st, 2020
On-demand labour platforms have been digitally transformed, featuring an increasing reliance on data analytics and algorithmic decision-making. Novel technologies as such have brought forth unprecedented commercial success, but the implications for job quality, well-being and worker rights are barely considered in platforms’ decision-making. In contrast to the massive volume of data collected or generated by the platform, the information available to gig workers has been deliberately kept to a minimum. This asymmetry of information enables the platform to manipulate its ‘human-data capital’ through precarious schemes carefully designed and displayed. For gig workers, the lack of information squeezes the room to manoeuvre; they incrementally found themselves working purely for the platforms, hence diverging from the original intention to have more flexible work patterns.
Meaningful data access and/or data portability is crucial for gig workers to understand their work and arrange it wisely. In the European Union, there are burgeoning legal regimes facilitating reverse data flows from platforms to individuals. For instance, the General Data Protection Regulation allows individuals to request a copy of personal data, or transmit to other places. Parallel schemes are taking shape in the UK and other member states that facilitate the porting of non-personal data for consumers (the Digital Content Directive) as well as professional users (the Free Flow of Non-Personal Data Regulation). Further to a sibling paper on these schemes’ applicability in the gig economy, this interdisciplinary research inquires how large-scale accumulation of data, coupled with algorithmic decision-making, has affected those working in the gig economy. Taking Deliveroo as an example, it further examines how the data can be assessed, aggregated and reused by food couriers to better their conditions.
With the evolving legal landscape in mind, we propose a structural response to algorithmic control over gig workers. Within a platform, a gig worker may leverage GDPR rights to obtain data of evidentiary value and contest algorithmic decision-making. Between platforms, a worker may wish to switch from one platform to another, or dwell on several at the same time. Data portability and interoperability are of critical use to ensure these processes. Beyond platforms, we explore the idea of data-driven worker science, a conceptual underpinning for worker-side innovations as well as data-driven organising demands.
Karen is Lecturer in Digital Sociology and Programme Director of the MSc Digital Society in the department of sociology at the University of Edinburgh. She describers herself as a 'digital' sociologist, ethnographer, and lecturer.
She currently at works on a research project that examines the
possibilities for solidarity in a digital economy, conducting interviews
among Deliveroo riders in Scotland. She is particularly interested in
the notion of "resilience" and the ways in which everyday working people
navigate shifting economies and technological terrains. She also works
on new and emerging digital research methods and research ethics in
digital scholarship. and digital labor in higher education.