Studying how Precarity, Technology, and AI intersect

by Yesim Kakalic and Jamie Hancock

INCLUDE+ has been mapping the landscape of research on the intersections between precarity, technology, data, and AI (Artificial Intelligence). Precarity has become a pressing concern amid the accelerating integration of technology, data, and AI in various domains of society. Debates about the interplay between technology and aspects of everyday life have prompted extensive research into this area. But, as we found, the results are concerning.

We are witnessing new forms of precariousness and the intensification of existing concerns as our societies continue to rapidly deploy new data-driven technologies. This digital transformation, leading to the ‘datafication’ of individuals, creates new layers of complexity and potential vulnerability in our interactions with technology. These changes are poised to significantly reshape our collective future. However, technological transformations don’t affect everyone equally. Marginalised groups — such as women, ethnic minorities, migrants, young people, and disabled individuals, and those experiencing socio-economic disadvantage– often find themselves disproportionately impacted. This trend is exacerbating existing social and economic disparities. As a result, ongoing research into precarity is vital.

What is precarity?

‘Precarity’ is a broad concept commonly used across the social sciences to discuss circumstances characterised by uncertainty, instability and risk. The term has been applied across many disciplines, including sociology, anthropology, geography, and economics. Researchers have brought a plethora of perspectives to the subject — from its social and cultural dimensions to its role in labour markets and its effects on individuals.

Any one person can experience precarity across multiple domains in their lives: in their livelihoods, housing, community ties, access to social support, access to utilities, access to infrastructure, access to resources, and even their relationship with the environment. These conditions all come with negative consequences: reduced well-being, increased stress, frayed communities, and limited socio-economic opportunities. Crucially, precariousness is not experienced equally: it disproportionately affects marginalised and vulnerable groups such as women, racial/ethnic minorities, migrant communities, disabled people, and young people, as well as those impacted by socio-economic class and status differences.

As an idea, precarity has increased in prominence over the past two decades. Key drivers for this include: the spread of neoliberal approaches to government worldwide and the aftermath of successive financial crises. Neoliberalism refers to a suite of policies which typically involve privatising public services, deregulating markets, weakening labour protections, restricting welfare entitlements, and policing welfare recipients more closely. Such policies have been associated with a significant increase in socio-economic inequality and insecurity since their first implementations in the 1970s.

Successive economic crises, from the 2008 crash to the COVID pandemic, have deepened these effects. High levels of indebtedness, rising living costs, and changes in employment markets have contributed to a sense of instability for an increasing number of people. Unemployment is only one factor here. Even when unemployment is (according to specific measurements) ‘low’, precarity remains a defining feature of the labour market across many ‘advanced’ economies.

In short, the rise in precarity worldwide thus reflects not just the hardship of reduced access to resources. It is part of a larger shift in socio-economic dynamics under neoliberal policies, including the increasing precariousness of labour markets even amidst employment stability.

Precarity is intertwined with technology, data, and artificial intelligence

Our project aims to shed light on the diverse ways in which the use and control of particular technologies can both exacerbate and mitigate precarity. Our survey of literature and relevant case studies, considers how the connections between precarity, technology, data and AI revealed a complex web of issues. These range vastly: from ‘gig’ work platforms like Uber and microtasking services like Amazon’s Mechanical Turk, to data labelling in AI supply chains, to algorithmically controlled government service provision and police surveillance.

Case Studies: Migration and Labour

Technological advancements — particularly ‘big data’ systems, algorithmic management tools, and AI — have both shaped and been shaped by the ways these conditions have affected labour. And in many cases, migrant communities are the ones most impacted. First, many of the latest hardware and software developments rely on undervalued labour, whether in factories to produce microchips or for data processing to supply AI. A large number of the digital systems that people in the Global North interact with appear to be autonomous when, in reality, they often depend on underpaid and under recognised workers in the loop. This extends beyond low renumeration: the often-invisible workforce powering digital systems regularly contends with precarious job security, demanding working conditions, lack of benefits, intrusive surveillance, and insufficient recognition for their critical contributions. Meanwhile, there are indications that some new data technologies might be leveraged by actors such as employers and tech companies to intensify surveillance and control across society. There is also a trend with digitised services — including ‘gig’ platforms like Uber — which may attempt to reposition workers as ‘self-employed’ entrepreneurs, paid per task. These platforms, in some cases, may circumvent certain labour laws, while in others they might outsource to regions with less regulations — a practice often referred to as regulatory arbitrage. This could potentially create an environment that challenges worker organising.

As labour markets have digitised and gig platforms have proliferated, so too have the numbers of people experiencing significant uncertainty and instability in their incomes. While developments like these can offer flexibility and autonomy, they also give rise to precarious labour conditions by reinforcing exploitative and insecure employment arrangements.

However, none of these phenomena is historically novel. Mass-produced technologies have relied on low-wage or unpaid labour since the late 18th century. Surveillance and performance metrics have long been facets of labour relations. Informal, piece-rate, and short-term labour have historically been the norm for most people globally, rather than permanent contract-based employment. What differentiates the current era is how today’s technologies enable these practices at an unprecedented scale and intensity, exacerbating insecurity across ever-more domains of life.

In all these cases, marginalised groups such as migrants, women, young or disabled people bear the brunt of the inequalities. However, a critical layer of precarity that pervades these categories, and extends beyond them, is socio-economic class. Inequality grounded in socio-economic class can foster precarious conditions for workers of all backgrounds, irrespective of other categorisations. Considering migrants, for instance, who are a globally vulnerable group, we find that their additional layers of insecurity often intersect with their socio-economic status. Their movement and behaviours are persistently monitored, and societal and legal barriers often relegate them to precarious roles that are unsuited to their skills. Many gig economy workers are migrants, often enduring substandard working conditions, extreme surveillance, and control, thereby making them more susceptible to exploitation. Similar dynamics of class-related precarity, albeit varying in specifics, also mark the precarious work conditions of women, young individuals, and those with disabilities. This intersection of socio-economic marginalisation, with other forms of vulnerability, underscores the intricate interplay of factors contributing to the wide spectrum of precarity in contemporary society.

Why is this research important?

Research is vital to enable us to understand the complex intersections between precarity, data and technology (including AI). This understanding can form the basis for the development of effective interventions to alleviate the negative effects of precarity.

Communities, campaigners, and labour organisers can use research to advocate for better conditions. Research can also assist informed policy interventions. Furthermore, such research can help influence the design and development of technology itself, to account for precarity (and other related) concerns. By understanding how the use and control of particular technologies can exacerbate precarity, we can identify opportunities toto leverage technology in ways that help to mitigate these issues. But in the process, it is essential that research investigates precarity through an intersectional lens. By doing so, systemic biases can be challenged and addressed, ensuring that technological advancements serve all sections of society, not just a privileged few.

At INCLUDE+, we see our research as a pivotal step towards bridging the gap between technological progress and social equity. It’s about shifting the narrative from technology as being one of the sources of precarity to technology as a tool for reducing precarity and promoting socioeconomic progress. By acknowledging and addressing these challenges, society can move towards a future where technology truly enhances lives, rather than exacerbating vulnerabilities.