Introduction

The Future of Work is an age-old fascination: with every new wave of technological innovation comes a series of thorny questions about its impact on the labour market. Will jobs be replaced by the new technology? If not, how will they be reshaped? What are the broader implications, both for individual workers, and legal regulation more generally? Recent technological advances have brought many of these questions back to the fore, notably in the context of the gig economy, enabled by mobile phones equipped with powerful processors, fast internet connections, and highly accurate satellite navigation. The labour market challenges inherent in a world of platform-based labour intermediation are considerable, from worker classification and collective rights protection through to health and safety, tax, and social security provisions. These issues have rightly been at the forefront of courts’ and policy makers’ attention, both domestically and at the international level.

Decision making and the gig economy

At the same time, however, a detailed exploration of the gig economy soon encounters a fundamental innovation paradox. Whilst it is undoubtedly true that key (technological) elements behind the rise of the gig economy are completely new phenomena, their impact on work organisation can more accurately be characterised as the logic continuation and extrapolation of long-existing trends in non-standard work.

On-demand work in the gig economy shares many dimensions with these forms of non-standard work: it is, in other words, a combination and rebranding of long-established models. Technology plays a crucial role in enabling the gig economy’s persistent growth – but despite the occasional call for a complete overhaul of applicable laws and regulations, it is increasingly becoming clear that the primary policy solution lies in the consistent application and enforcement of existing regulatory models.

Algorithmic decision making

Can the same conclusions hold true for the rise of artificial intelligence in general, and the deployment of increasingly sophisticated machine learning algorithms in particular? My early work on this topic suggests that they do not: at least some of the changes which this latest wave of automation will bring to the world of work require a fundamental rethink of key elements of the traditional apparatus of employment law and labour market regulation. This is not, however, due to the much-vaunted rapid displacement of employment and the consequent need to tackle mass technological employment. Instead of taking away workers’ jobs, I have become convinced, advances in AI-driven decision-making will first and foremost change their managers’ daily routines, augmenting and eventually replacing human day-to-day control over the workplace: we are witnessing the rise of the ‘algorithmic boss’.

In order to substantiate this claim, much work lies ahead. First, in terms of engaging with the economic literature of technological unemployment: common strands running through historical debates about automation-induced job losses, including an emphasis on substitution over complementarities and a tendency to ignore or underestimate the potential for creating novel jobs, in order to question the most recent wave of unemployment predictions. This is not to say that technological change will not bring about fundamental upheavals at work, from a dramatic increase in available information and the rapidly falling cost of processing power to new and evolving forms of exercising control. Taken together, these factors have already begun to enable the automation of decision making across the lifecycle of the employment relationship.

What AI-driven decision making looks like

Present space limitations prohibit detailed engagement with specific case studies. Suffice it to say that both start-ups and well-established operators have already demonstrated how AI-driven software packages are able to augment, or even replace, traditional management in the exercise of the full range of employer functions, from digital reputation screening and CV filtering to on-going job instructions, performance monitoring, and termination decisions.

This is not merely a return to (digital) Taylorism: both the kinds of data considered and the probabilistic patterns relied upon in machine learning fundamentally differ from the traditional management structures around which employment law has been designed. The resulting regulatory challenges are manifold: at the very least, we require a three-fold structure of analysis, first exploring the implications of new forms of data collection and organisation for privacy and data protection, before turning to the implications of AI processing and control, including ex post facto explicability and the cross-jurisdictional scaling of successful machine learning algorithms.

Conclusion

It is there that we encounter genuinely novel questions: the large body of scholarship exploring the ascription of employer responsibility has always proceeded on the basis that the issues at stake are legal ones – whether in sham contracting or (ab-)uses of corporate personality – and thus, at least in principle, amenable to legal solutions drawing on the same bodies of doctrine. The diffusion of responsibility inherent in AI decision making, on the other hand, is ultimately as much a technical challenge as it is a legal one.

Jeremias Adams-Prassl is Professor of Law at Magdalen College, Oxford. He is particularly interested in the future of work, and frequently advises governments, international organisations, and the private sector on navigating the challenges and opportunities arising from the impact of technology on labour markets. His most recent book, Humans as a Service: The Promise and Perils of Work in the Gig Economy (OUP 2018) won the 2019 St Petersburg International Legal Forum Prize. Jeremias tweets @JeremiasPrassl