AI is built by the capitalist exploitation of the marginalized workers, disproportionately harms minority communities.

Labor Tax: How Underpaying Minority Workers Fuels AI Development In their investigative piece “The Exploited Labor Behind Artificial Intelligence," Williams, Miceli, and Gebru [16] shed light on the harsh realities faced by gig workers, essential in the AI industry, and disproportionately drawn from vulnerable populations. Big tech players such as Amazon and Facebook heavily rely on these laborers, including data labelers, content moderators, and delivery drivers. These workers, despite their indispensable contributions, often endure abysmal pay, constant surveillance, unrealistic quotas, and even safety hazards. Shockingly, data labelers, responsible for critical AI training data, can earn as little as $1.77 per task. In the troubling case of content moderators in Kenya, they are relentlessly monitored to make split-second decisions, even when confronted with disturbing content. These workers, whose labor sustains billion-dollar companies, are unjustly compensated. Izaguirre [7] discussed a very recent example of labor exploitation within the gig economy from the ridesharing giants, Uber and Lyft. In New York, these companies are set to pay a staggering $328 million to settle complaints that they wrongly imposed taxes and fees on their drivers that should have been covered by passengers. State Attorney General Letitia James unveiled this settlement, emphasizing how Uber and Lyft had systematically deprived their drivers of millions of dollars in earnings and essential benefits. The drivers, akin to those engaged in data labeling for AI, toiled for long hours in challenging conditions while being undercompensated. The repercussions of such labor exploitation have wide-ranging implications, demonstrating the persistence of these issues across different sectors of the gig economy. These drivers, often working long hours in challenging conditions, waited an arduous eight years for justice, emphasizing the enduring time burden in their battle for rights–we’ll will explore the ‘time’ dimension in the next subsection. Moreover, racial capitalism also rears its head in other ways in generative media AI applications. Startups have built systems to synthesize fake profile photos by exploiting images of minority models without consent or payment. The marginalized provide raw material|their unvalued data and likenesses|to advance technologies from which they do not benefit. An example of this is the Argentina-based design firm, Icons8, which built generative AI systems to synthesize fake profile photos by exploiting minority likenesses [6]. Though the models’ images provided core training data, they received no payment for this labor, nor any share of the systems’ profits derived from their inputs. Once again, capitalism fueled AI development by commodifying bodies. The models’ data and likenesses became raw materials for tech advancement from which they did not equally gain. This case provides further evidence that we must scrutinize and address the human exploitation underlying much AI progress. Time Surcharge: The Burden on Minorities to Fix Biased Systems In 2019, a pivotal federal study by NIST unveiled the entrenched racial biases within facial recognition algorithms, revealing higher false positive rates for Asian, African American, and Native American faces compared to white counterparts [5]. This disparity is not merely a technical oversight but a reflection of a broader societal issue where fairness is often assessed through a predominantly white lens, perpetuating and amplifying harmful racial stereotypes. These algorithms, failing more frequently with Black and Asian individuals, not only reinforce the misconception of homogeneity within these communities but also echo Cedric Robinson’s argument that capitalism morphs pre-existing social differences into racial disparities, using technology to further these divides. These biases extend beyond race to gender and age, with algorithms showing elevated error rates for women, the elderly, and the young compared to middle-aged white men. Simone Browne, in Dark Matters [2, p. 113], elucidates how these systems perpetuate stereotypes, such as misclassifying Asian males as females and Black women as men, illustrating how AI, like capitalism, disseminates racial and gender stereotypes on a massive scale under the guise of technological neutrality. The case of Brandon Mayfield, as discussed by Browne , exemplifies how biases in AI can have real-world consequences, where erroneous algorithmic matches based on fingerprints can lead to wrongful accusations, as seen in the 2004 Madrid bombing investigation [2, p. 115]. Mayfield’s wrongful identification highlights how minoritized groups not only bear the brunt of these biases but also face a "Privacy Levy," paying a significant personal and financial price due to prejudiced technological systems. This systemic bias embedded within AI technologies serves capitalist ends in several ways. Firstly, it perpetuates a cycle where the burden of correcting and navigating biased systems falls disproportionately on marginalized communities, consuming their time and resources| a “Time Surcharge" that benefits the status quo by maintaining existing power dynamics. Secondly, the deployment of such technologies, especially in law enforcement and surveillance, opens lucrative markets for tech companies, capitalizing on the state’s desire to monitor and control, often at the expense of those already marginalized. In essence, these biased algorithms are not just products of existing societal inequalities; they are tools that capitalism exploits to further entrench its dominance, ensuring that the costs of innovation are borne by those least able to bear them, while the profits accrue to those already in positions of power. Thus, the development and implementation of biased AI systems are stark manifestations of racial capitalism, where technological advancements are leveraged to amplify and profit from racial and social disparities. The need for a concerted effort to address these biases is not just a matter of technical accuracy but of challenging the capitalist structures that incentivize and benefit from the perpetuation of inequality. The Wellbeing Toll: How AI Progress Erodes Minority Health The advancement of AI technology, hailed as a cornerstone of modern innovation, often conceals a human toll disproportionately borne by marginalized communities. D’Ignazio and Klein’s observation [3, p. 183] that the tech industry relies on the precarious labor of older women of color, contrasts sharply with the privileged demographics of Silicon Valley. This labor force, essential to refining AI technologies, faces not just economic exploitation but significant mental health challenges, as Billy Perrigo’s investigations [12, 13] into the conditions of data labelers reveal. Workers engaged in making AI systems safer endure traumatic content exposure for meager wages, suffering profound mental health impacts without adequate support. This reality underscores a capitalist calculus where AI progress is prioritized over the well-being of those who enable it, revealing a stark exploitation that benefits technological advancement and profit margins at a significant human cost. In her work Automating Inequality [4, p. 173], Virginia Eubanks provides comprehensive evidence of the discriminatory outcomes resulting from automated social services. One striking example is the case of Angel and Patrick’s family. Angel seeking help from a counselor and taking medication to manage her PTSD is undoubtedly a responsible decision aimed at becoming a better-equipped caregiver for her children. However, her use of public assistance|due to their socioeconomic status|to access these essential services has an unintended negative consequence. It affects the family’s AFST (Allegheny Family Screening Tool) score, putting them at risk of having social services intervene and potentially remove their children. This situation raises a critical question: Why should it be a choice between the parents’ well-being and the children’s welfare? Instead, it should be an ‘and’ rather than an ‘or,’ emphasizing the importance of supporting families as a whole. Moreover, this scenario exemplifies the “Privacy Levy" where the act of seeking private counseling and prescriptions wouldn’t flag the same concerns, highlighting the disproportionate burden placed on marginalized communities. These instances reflect a broader trend of "algorithmic exploitation," where the relentless pursuit of efficiency and profit in AI development and application exacerbates social disparities. This exploitation is twofold: firstly, it leverages the labor of marginalized workers under deplorable conditions, enhancing AI capabilities while neglecting the workers’ health and economic stability. Secondly, it deploys these technologies in ways that disproportionately harm minority communities, whether through surveillance, law enforcement, or social services, thereby reinforcing systemic inequalities. The capitalist benefit derived from these practices is multifaceted, encompassing direct economic gains from reduced labor costs and expanded market dominance, as well as indirect advantages through the perpetuation of a socio-economic order that maintains a readily exploitable workforce. The prioritization of technological advancement and profit over human dignity and equity reflects a fundamental misalignment of values, where the potential of AI to serve the common good is compromised by a capitalist ethos that values profit above people. Addressing these issues demands a reevaluation of the objectives and ethics guiding AI development and deployment. It necessitates a shift towards a more equitable approach that recognizes the intrinsic value of all labor, respects the dignity of every individual, and prioritizes the well-being of marginalized communities. Only through such a transformative shift can we harness the potential of AI to contribute positively to society, rather than perpetuating the injustices of a capitalist system that exploits the vulnerabilities of the least among us. Conclusion This comprehensive exploration reveals compelling evidence that contemporary AI development mirrors and perpetuates the exploitation and marginalization of minoritized groups witnessed under historical racial capitalism. AI not only reflects but also concentrates deep societal biases ingrained in our structures. Despite promises of progress, this emerging technology entrenches social divides, much like capitalism historically intensified differences into racialized hierarchies. Uncontrolled AI poses a significant threat, exacerbating current oppressions and expanding racial disparities across dimensions such as labor, rights, and well-being. Undervalued minority workforces fuel systems they cannot equally access or shape, while flawed data and algorithms covertly deny opportunities and resources. The relentless pursuit of AI innovation disregards the resulting trauma among marginalized builders. In our extensive review of AI’s role in perpetuating historical inequalities, we’ve laid the foundation for understanding the intricate intersection of technology, capitalism, and social justice. We’ve discussed how labor exploitation, unequal time burdens, erosion of minority well-being, and the violation of personal privacy are all interconnected threads in the complex fabric of AI development. These dimensions are not isolated; they intersect and amplify each other, perpetuating historical inequalities. To truly address these issues, it is crucial to recognize that the exploitation of marginalized communities affects every aspect of AI, from its inception to its implementation. As we navigate the modern frontier of AI exploitation, the goal remains to inform and create awareness about the profound impact of technology on minoritized communities. Without diversity, inclusion suffers. Rather than rushing to ‘Move Fast and Break Things’, like Ruha Benjamin [1], we encourage you to move slower, fix the broken processes, and empower people.