Paper session 5: Future of work

Algorithmized but not Atomized? How Digital Platforms Engender New Forms of Worker Solidarity in Jakarta

ABSTRACT. Jakarta’s roads are green, filled as they are with the fluorescent green jackets, bright green logos and fluttering green banners of basecamps created by the city’s digitized, ‘online’ motorbike-taxi drivers (ojol). These spaces function as waiting posts, regulatory institutions, information networks and spaces of solidarity for the ojol working for mobility-app companies, Grab and GoJek. Their existence though, presents a puzzle. In the world of on-demand matching, literature predicts an isolated, atomized, disempowered digital worker. Yet, ojol basecamps persist both in the physical world and digital realm, complete with quirky names, emblems, social media accounts and even their own emergency response service. In fact, their solidarity actively revolves around the identity of using a smartphone to generate employment, and a key part of the imagined networks of community occur online through WhatsApp groups. This paper asks, under what conditions are digital workers able to create collective structures of solidarity, even as app-mediated work may force them towards an individualized labor regime? Are Jakarta’s digital labor collectives a reflection of its social context, a product of technological change, or a result of interactions between both? Increasingly, academic world has started paying attention to forms of digital labor organization. The aim of this project is to empirically tease out the bi-directional conversation between globalizing digital platforms and social norms, civic culture and labor market conditions in non-western urban spaces, which allow for particular forms of digital worker resistances to emerge. It recovers power for the worker, who provides us with a path to resisting algorithmization of work while still participating in it.

Learning Occupational Task-Shares Dynamics for the Future of Work

ABSTRACT. The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations’ underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.

Does AI Qualify for the Job? A Bidirectional Model Mapping Labour and AI Intensities

ABSTRACT. In this paper we present a setting for examining the relation between the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated scenarios. We perform a mapping between labour and AI using a set of cognitive abilities as an intermediate layer. This setting favours a two-way interpretation to analyse (1) what impact current or simulated AI research activity has or would have on labour-related tasks and occupations, and (2) what areas of AI research activity would be responsible for a desired or undesired effect on specific labour tasks and occupations. Concretely, in our analysis we map 59 generic labour-related tasks from several worker surveys and databases to 14 cognitive abilities from the cognitive science literature, and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. We provide this model and its implementation as a tool for simulations. We also show the effectiveness of our setting with some illustrative examples.