Technology is often seen as the great equalizer, capable of transcending social and political biases to provide information and opportunities to all. Yet, the systems we design are frequently prone to their own forms of racial discrimination, shaped by the biases, assumptions, and systemic inequities of the societies in which they are created. This phenomenon, known as techno-racism, refers to the ways in which technology perpetuates or exacerbates racial inequalities. Algorithms used in hiring, policing, or lending decisions can inadvertently encode racial biases. For example, facial recognition systems have been shown to misidentify individuals with darker skin tones at significantly higher rates. These examples highlight how technology, far from neutrality, can reinforce and deepen societal divides.
Language learning models (LLM) are a type of AI that is used to recognize and generate text based on programmed speech patterns. The Stanford Institute for Human-Centered AI contextualizes the extent of LLMs as they are “incorporated into decision-making systems for employment, academic assessment, and legal accountability.” A study published in the journal Nature illustrated that these models can exhibit racial biases, particularly when processing text written in different dialects, such as African American English (AAE) versus Standard American English (SAE). Researchers tested LLMs through matched guise probing, meaning they gave AI text and speech samples and asked them to make decisions about what attributes the person with the dialects had. In addition, they had AI make decisions about job acceptances, legal judgments, and essay grading based on the traits they attributed to the individuals with these dialects.
The results showed that LLMs consistently interpreted AAE more negatively, leading to unfair outcomes—like lower hiring recommendations, poorer grades, and harsher legal judgments—compared to identical content written in SAE. When asked to describe the appearances of people based on the dialects, the algorithms generated descriptions that aligned with historically racist stereotypes, often portraying AAE speakers as less intelligent, less professional, or even more criminal compared to SAE speakers. This bias reflects harmful stereotypes present in the data used to train these models.
The study is significant because it highlights how AI systems, if not carefully designed, can perpetuate racial inequalities in high-stakes areas like hiring, education, and the legal system. To address this, researchers recommend using more diverse training data, developing tools to detect biases, and adding human oversight to ensure fairness. This work underscores the urgent need to make AI systems equitable and free from racial prejudice as their influence continues to grow in decision-making processes that impact millions of lives.
The algorithms we use to make objective decisions about our lives are responsible for perpetuating racist assumptions about people. This happens because these algorithms are often trained on data that reflects historical and societal biases, such as associating certain dialects, names, or neighborhoods with negative stereotypes. This leads them to make decisions that hurt minorities by perpetuating cycles of poverty and limiting chances for financial growth. For instance, mortgage algorithms help decide rates for loan applicants. Most of these algorithms are trained on data taken at a time when African Americans weren’t allowed to own property. “In 2019, a study by UC Berkeley researchers found that mortgage algorithms show the same bias to African American and Hispanic borrowers as human loan officers,” costing minorities nearly half a billion dollars in loans each year, reports the Syracuse University Journal of Science and Technology Law.
Techno-racism is more covert, mostly going unnoticed by those who are affected by prejudice-trained algorithms. From facial recognition software misidentifying minorities as criminals to social media algorithms flagging content creators of color at a disproportionate rate, the impact of biased technology is pervasive and deeply ingrained in the digital systems we rely on daily. Unlike overt forms of discrimination, techno-racism operates in the background, shaping opportunities, perceptions, and even life-altering decisions without those affected always realizing it. As technology continues to occupy more and more areas of our lives, the best way to stop techno-racism is by demanding transparency in how algorithms are designed, holding companies accountable for biased outcomes, and prioritizing diversity in the teams that build and regulate these systems. This includes auditing datasets for biases, creating tools to detect and correct unfair practices, and involving marginalized communities in the development process. By addressing these issues head-on, we can ensure that technology serves as a tool for equity rather than a hidden force of oppression.
