The platform economy, labour and Covid-19

On 18 September 2020, I present my research on the platform economy and its impact on labour in Covid-19 times at Nantes Digital Week, as part of a special event organized by CGT, a Union.

The mobility restrictions that accompanied the pandemic encouraged use of digital tools to socialize, study and work, suggesting that automation is gaining ground and that technology enables contactless – hence safe – interactions in much of our social life. Yet behind apparent automation, precarious and unprotected human labour is hidden. Workers recruited through digital platforms to make these solutions work, are in fact disproportionately exposed to risks. I illustrate these ideas in three main cases: food delivery workers, that enabled the restaurant industry to stand the crisis even during lockdown; commercial content moderators that are to return to office sooner than others, to protect our safety online; and AI micro-workers who trained tools whose sales have gone up during stay-at-home rules, such as voice assistants, and helped the creation of datasets for much-needed health applications.

Digital inequalities in time of pandemic

Just published a new, collective paper on new kinds of risk that are emerging with the COVID-19 virus, arguing that these risks are unequally distributed. Digital inequalities and social inequalities are rendering certain subgroups significantly more vulnerable to exposure to COVID-19. Populations bearing disproportionate risks include the social isolated, older adults, penal system subjects, digitally disadvantaged students, gig workers, and last-mile workers. We map out the intersection between COVID-19 risk factors and digital inequalities on each of these populations in order to examine how the digitally resourced have additional tools to mitigate some of the risks associated with the pandemic. We shed light on how the ongoing pandemic is deepening key axes of social differentiation, which were previously occluded from view.

These newly manifested forms of social differentiation can be conceived along several related dimensions. At their most general and abstract, these risks have to do with the capacity individuals have to control the risk of pathogen exposure. In order to fully manage exposure risk, individuals must control their physical environment to the greatest extent possible in order to prevent contact with potentially compromised physical spaces. In addition, they must control their social interactional environment to the greatest extent possible in order to minimize their contacts with potentially infected individuals. All else equal, those individuals who exercise more control over their exposure risk — on the basis of their control over their physical and social interactional environments — stand a better chance of staying healthy than those individuals who cannot manage exposure risk. Individuals therefore vary in terms of what we call their COVID-19 exposure risk profile (CERPs).

CERPs hinge on pre-existing forms of social differentiation such as socioeconomic status, as individuals with more economic resources at their disposal can better insulate themselves from exposure risk. Alongside socioeconomic status, one of the key forms of social differentiation connected with CERPs is digital (dis)advantage. Ceteris paribus, individuals who can more effectively digitize key parts of their lives enjoy better CERPs than individuals who cannot digitize these life realms. Therefore we believe that digital inequalities are directly and increasingly related to both life-or-death exposure to COVID-19, as well as excess deaths attributable to the larger conditions generated by the pandemic.

The article has been published in First Monday and is available here.

In the same special issue of First Monday, I co-published two reference articles:

Digital inequalities 2.0: Legacy inequalities in the information age

Digital inequalities 3.0: Emergent inequalities in the information age

The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence

New article, co-authored with Antonio A. Casilli and Marion Coville, just published in Big Data & Society!

The 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. We uncover 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.

The three main functions of micro-work in the development of data-intensive, machine-learning based AI solutions.

The paper reports results of the 2017-18 DiPLab project, and is available here in open access.

Back from Reshaping Work 2018

I was last week at the second Reshaping Work in the Platform Economy in Amsterdam. The interest of this small conference is tht it brings together different actors of the platform economy, from academics and students to policymakers, union leaders,  workers, and representatives of platforms to discuss.

In an overview of preliminary results of our project DiPLab, Antonio A. Casilli and I presented our reflection on how micro-work powers artificial intelligence (AI), in three main ways:

  1. Training AI
  2. Validating outcomes of AI
  3. Impersonating AI when it is cheaper or simpler that real AI

AI

No more details for now… it will come out as a working paper very soon!