Thinking and Reading at the Intersection of Labor, Race, and Tech

The rise of data-centric technologies is an opportunity for the labor and racial justice movements to build new bridges.

Data & Society
Data & Society: Points
9 min readJan 18


Image: Gloria Mendoza

Although the labor and racial justice movements have overlapping goals, their work has often been siloed. But with data-centric technologies increasingly impacting both movements, there are opportunities for them to build new bridges.

With this in mind, over the past year, Data & Society’s Labor Futures team gathered an interdisciplinary group of people — representing a number of tech, labor and racial justice-focused organizations — to create a space for open discussion about how the movements can work together to address the impacts of technology on marginalized communities. The group met monthly to discuss issues at the intersection of labor, race, and technology, and to share our respective research. Through this exploration, we sought to reflect on these movements’ shared values, acknowledge divergences, and envision a shared path forward.

In an effort to continue and broaden these discussions and highlight phenomenal scholarship at this intersection, we asked participants to share a few readings that have contributed to their understanding of the two coinciding movements, and have influenced their work.

Cynthia Khoo, The Center on Privacy & Technology at Georgetown Law

This article by Raval and Pal, and the podcast interview with Raval, expanded for me the scope of issues to consider when thinking about the “gig economy” and what it means for workers. In their article, they provide an example of gig work scholarship that departs from conventional narratives and research which, according to the authors, demonstrate what they call “the Uber bias.” While such scholarship has reflected rideshare and delivery apps’ disproportionately male demographics, Raval and Pal’s work focuses on an app-based work sector that is disproportionately female as well as racialized — ”beauty and wellness services” in India.

They highlight how various platforms’ features or requirements play out in female Indian workers’ lives in practice, for good or ill, involving both non-economic and economic values. For instance, the discussion of “professionalization” reveals intersectional dimensions where company control and respectability politics operate as a form of limited protection for these workers against societal stigma and sexual harassment.

In her Tech Won’t Save Us interview with host Paris Marx, Raval points out the highly contextual nature of even foundational framings of issues, noting that the ​term “gig ​work” does not really resonate in the Indian context where ​”informal” and “piecework” employment has long been prevalent, acknowledged, and openly incorporated into the labor economy, even if it was not always app-based. She further emphasizes that “gig work” is not ​particularly novel​ when separated from its technological veneer, citing the US National Domestic Workers Alliance’s declaration that domestic workers are “the original gig workers.”

Gabrielle Rejouis, independent researcher

An understanding of how tech replicates racial and economic inequality shapes how I approach “the future of work.” The concerns raised by contemporary labor advocates about the gig economy have impacted Black workers and workers of color in traditional jobs for generations. New technologies hide the exploitation and destabilization these communities face behind allegedly neutral and unbiased systems. To debunk tech’s arguments and false solutions, historical grounding is essential.

Dr. Safiya Umoja Noble’s Algorithms of Oppression shows how technology can preserve the racial inequalities we see offline. This book was key for me to connect digital discrimination with the racial bias we combat with civil rights laws in analog, or non-digital, spaces. The book outlines how, without intervention, tech accelerates discrimination for Black communities and other communities of color.

When I look at how technology is transforming labor, I examine how it transcribes racial discrimination. For example, stories of gig companies stealing tips from workers must be considered in the history of tipped wages and wage theft. Tipping in the United States emerged after the Civil War and allowed white business owners to reduce the wages of Black workers: rather than earn a liveable wage, Black workers relied on the tips of customers. Today, women of color are more likely to earn tipped wages.

In the gig economy, we see stories of tipping being used to help workers supplement their take home pay. These companies misclassify their workers to avoid paying them a liveable hourly rate. However, opaque algorithms hide — both from workers and customers — how tips are used, making it easier for gig employers to steal wages. Instacart, Target’s Shipt, and Amazon Flex have reportedly used tips toward their guaranteed non-tipped wages or withheld tips altogether. Since they participate in the gig economy at a higher rate, these practices will disproportionately harm workers of color.

I encourage advocates to dig into the history of racialized labor violations as they research and challenge the future of work. Racial justice frameworks are crucial to protect workers from attacks on their labor protections. Contemporary intersections of labor, race, and technology must take into account how employers present old tricks in new tech. Racial justice frameworks are vital to disrupt further erosion of equity.

Ireti Akinrinade, Data & Society’s Health and Data team

This report from Data for Black Lives refers to data capitalism as “the economic model built on the extraction and commodification of data and the use of big data and algorithms as tools to concentrate and consolidate power in ways that dramatically increase inequality along lines of race, class, gender and disability.” The history of racial discrimination, data collection, and technological deployment is especially present in labor relations. Employers deploy technology, especially through automated management, to increase productivity, disempower workers, and evade responsibility for inhumane worker treatment under the guise of efficient workplace decision-making. The report identifies major issues with technological innovation driven by data capitalism, including how racism is encoded behind the black box of proprietary algorithms and how power is biased toward employers as employees take on more risk and responsibility. It shows how historical disparities are implicit in the data sets that are used to feed algorithmic decision-making, and how they are consequently reproduced in the consumer marketplace and beyond.

Milner and Traub’s synthesis between the historical context of profit extraction from the physical body and what we are bound to participate in today complexifies every attempt to study digital life. As I research how individuals and entities attempt to know people through their digital trails, I revisit this framework of data capitalism to tether my analysis of evolving infrastructures for digital behavioral analysis.

Urmila Janardan, Upturn

In his article, Nathan Newman discusses how the history of pre-employment personality tests are partially rooted in efforts to prevent unionization. Personality tests have been used to screen candidates for their willingness to agitate and organize for better working conditions since at least the 1970s. These tests can also result in disability discrimination, by asking questions about mood or outlook that are aimed to weed out “disgruntled” employees. Recent lawsuits against Target and CVS have required them to remove certain questions from their personality tests, but overall they are still widely used — by some estimates, 80 percent of Fortune 500 companies use personality tests for pre-hire screens. Ultimately, such tests do not correlate with job performance and instead serve to protect employers interests while denying workers much needed employment.

Pauline Kim’s law review article is a very useful introduction to how workplaces increasingly adopt algorithms to determine who gets hired, fired, promoted, etc, and explains how workforce analytics and data-driven decision-making impact equity in the workplace. Kim pulls apart assumptions that relying on data to make decisions will reduce human bias by showing all the ways data can and will distort employment decisions like hiring, firing, and promotions. Kim goes on to describe how the current Title VII legal regime might be used to guard against such discrimination, while also describing its limitations.

Eve Zelickson, Data & Society’s Labor Futures team

In “Technological Elites, the Meritocracy, and Post-Racial Myths in Silicon Valley,” Safiya Noble and Sarah Roberts examine how post-racialism is enacted and performed in the Silicon Valley ethos and the tech sector as a whole. The authors work to debunk the view of Silicon Valley as a meritocracy, describing it instead as a place where “bias is operationalized.”

Similarly, Tressie McMillan Cottom’s “The Hustle Economy” in Dissent focuses on the racial inequality embedded in the hustle economy, which McMillan Cottom describes as “a type of job-adjacent work that looks like it is embedded in the formal economy but is governed by different state protections, which makes the work risky and those doing it vulnerable.” She offers a rich description of how this particular type of labor extraction is racialized, as many digital platforms (especially in the fintech space) target marginalized populations.

Finally, because of its applicability to my current research on Ring doorbell cameras and delivery drivers, this paper about Nextdoor by Rahim Kurwa has enriched my thinking about how digital networks are tools for entrenching and often exacerbating racism in physical communities.

Korica Simon, Center on Privacy and Technology at Georgetown Law

In this article, Ajunwa addresses the history of slavery and the labor laws that followed its end. Some of these laws limited economic opportunities for people of color while also criminalizing them for their lack of economic opportunities. She considers the history of worker surveillance, including how companies would hire detectives to spy on their workers and have the detectives report back on any union activity. Now, companies have changed strategies; instead of having detectives in the workplace, they implement digital devices to spy on workers and kill union organizing. Ajunwa also explores the challenges that gig workers face and how the industry increases their marginalization within the labor market by not providing them with the tools they need to seek other kinds of employment. She writes about how Black and Latinx women occupy positions that are starting to see a high risk of automation, and that will eventually result in unemployment disparities. Ajunwa has written other articles on issues of labor, race, and tech, including “Protecting Workers’ Civil Rights in the Digital Age,” which focuses on the discriminatory effects of automated hiring practices.

Alexandra Mateescu, Data & Society’s Labor Futures team

Veena Dubal’s essay, Essentially Dispossessed, tries to make sense of the “cruel contradiction” faced by gig workers since the start of the COVID-19 pandemic, a result of fragmented labor law and platform companies’ uncompromising efforts to disenfranchise workers. When the pandemic hit, gig workers were hailed by government officials and the public as heroic essential workers necessary to keeping people safe and healthy. At the same time, they were left out of critical protections and benefits while being asked to put their lives at risk. Dubal describes the experiences of gig workers in Rideshare Drivers United, who mobilized and used their knowledge of the legal system to navigate contradictory and sometimes unworkable unemployment insurance claims processes while facing poverty, homelessness, and illness. This essay powerfully underscores how a workforce of primarily low-income and racially minoritized workers can be rendered disposable in moments of crisis, and how this was entirely normalized by government and corporate actors.

Jenn Stroud Rossmann and B.R. Cohen’s essay in FastCompany is a fantastic narrative about how the history of technology in the US is a palimpsest, where the lines of our railroad system gave way to the telegraph and then to digital communications, carrying through each layer the dynamics of settler colonialism and slavery, arduous labor, as well as land and resource dispossession toward the amassment of vast private fortunes. The heroic narratives that hailed railroad tycoons as shining envoys of Manifest Destiny have morphed into the contemporary myth of the lone tech genius and visions of unfettered technological progress. Erased from this history are the workers who actually built the railroads, the many Black Pullman porters who worked the passenger sleeper cars, and the contributions of people of color throughout STEM history and across the telecommunications industry.



Data & Society
Data & Society: Points

An independent nonprofit research institute that advances public understanding of the social implications of data-centric technologies and automation.