Artificial intelligence (AI) is often hailed as a tool that can bring efficiency, fairness, and progress. Yet, when we dig deeper into its design, application, and impact, it’s clear that AI is not neutral. Far from it. It reflects the biases and systemic inequities of the society that created it. And, for Black communities, this means that AI is perpetuating the same cycles of disadvantage that have existed for centuries, exacerbating racial disparities rather than eliminating them.
A glaring example of this is AI in recruitment, which has been increasingly used by companies to streamline the hiring process. AI-powered tools are now deployed to assess resumes, screen candidates, and even conduct initial interviews. On the surface, this technology seems like a solution to bias in hiring a way to remove human prejudice and ensure that everyone is evaluated based on their qualifications rather than their race or background. But in practice, these systems are built on data that is inherently biased, and as a result, they often perpetuate the same racial inequalities that they were designed to fix.
Hiring algorithms are trained on historical data data that reflects the biases of past hiring practices. For decades, Black individuals have been excluded from many opportunities or relegated to lower-paying, less prestigious roles due to systemic racism and discrimination. When these AI systems are trained on that data, they are learning to value the qualifications, experiences, and backgrounds that have traditionally been associated with white candidates. As a result, AI recruitment tools often favour applicants who fit a narrow and predominantly white standard. They favour candidates from predominantly white universities, those with work experience at certain companies that are historically less diverse, and those with backgrounds that reflect traditional norms in the workforce.
The impact on Black job seekers is profound. Studies have shown that AI recruitment tools are more likely to filter out applications from Black candidates because they don’t fit the narrow criteria these algorithms are trained on. For instance, if a job requires a certain set of “preferred” experiences or qualifications that reflect historical biases such as leadership roles that are overwhelmingly white or networks built within predominantly white institutions Black candidates may find themselves excluded, even if they are equally or more qualified.
In 2018, a study by ProPublica found that predictive hiring tools used by companies in the United States were more likely to penalize Black candidates compared to white candidates, even when their resumes were identical. These systems were less likely to advance Black applicants, reinforcing the racial disparities in hiring. This doesn’t even touch on the subtler ways in which AI impacts recruitment. In some cases, AI tools assess personality traits or “fit” with company culture, often based on data from past hiring patterns. These systems tend to favour candidates who fit the existing culture of the workplace, which, in most cases, is overwhelmingly white and male. As a result, Black candidates are often deemed a “poor fit,” simply because they don’t match the traits of those who were hired before them.
This issue doesn’t just affect the initial stages of recruitment. AI is also being used to determine promotion opportunities, assess performance, and predict career trajectories. But again, these tools are trained on biased data. For example, when an AI system is used to evaluate employee performance, it may use historical data that reflects the lower starting salaries and slower career progression of Black employees. As a result, Black employees may be unfairly penalized by these systems, even though they are performing at the same level as their white counterparts.
In healthcare, AI systems have been shown to perpetuate racial bias, resulting in poorer treatment outcomes for Black patients. These systems often use data from predominantly white populations, leading to less accurate predictions and diagnoses for Black individuals. Similarly, AI tools in finance used for credit scoring or loan approvals are often trained on data that disproportionately disadvantages Black people, leading to higher rejection rates for loans, mortgages, or credit applications for Black applicants.
But perhaps the most disturbing aspect of AI's role in recruitment is that it further entrenches existing power dynamics. Because AI is often seen as an objective, impartial technology, its biases are not always recognized. When a person uses an algorithm to make a hiring decision, they may feel that the decision is being made by a “neutral” machine, rather than by a biased individual. This illusion of impartiality can make it more difficult to hold companies accountable for their practices.
The danger here is that as AI continues to become more embedded in recruitment and hiring processes, Black job seekers will be even more at risk of being shut out of opportunities. The lack of diversity in the tech industry means that these biases will continue to go unchecked. According to a 2020 report, less than 5% of the tech workforce is Black, and that lack of representation translates into products and systems that fail to consider the needs and realities of Black people.
The role of AI in recruitment is just one example of how systemic racism is being encoded into technology. The problem is not the technology itself, but how it is being used to reinforce existing inequalities. Until we confront the biases embedded in the data that powers AI and work toward ensuring diverse representation in the development of these technologies, AI will continue to disadvantage Black communities. And as long as we allow AI to operate unchecked in areas like hiring, healthcare, and finance, we are building a future where systemic racism is not just perpetuated but amplified by the very tools that are meant to drive progress.
To address this, we need stronger accountability in AI development and deployment. Companies and governments must work to ensure that AI systems are audited for bias, that they are tested against diverse datasets, and that the people developing these systems reflect the communities they are meant to serve. There needs to be a concerted effort to build technologies that actively dismantle the barriers they perpetuate, not reinforce them.
It’s also crucial that we, as a society, stop viewing AI as the solution to our problems without recognizing the potential harm it can cause when built and applied without equity in mind. If we allow AI to continue reinforcing racial disparities, we risk creating a future where these biases are even more deeply ingrained, where entire generations of Black people are systematically excluded from opportunity by a machine that is supposed to be neutral.
AI is not just a tool; it is a reflection of our values. Until we ensure that it works for everyone, it will continue to work against those who need it the most.
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