The Forgotten Algorithms: How AI Models Mirror Human Bias in Ways We Don’t Talk About
AI models promise neutrality, but many silently inherit and amplify human bias. This article explores the overlooked mechanisms behind algorithmic prejudice.
Introduction: The Algorithmic Blind Spot
Artificial Intelligence is supposed to be neutral—a logic-driven alternative to flawed human judgment. Yet, as machine learning becomes embedded in hiring, policing, lending, and social media, an uncomfortable truth has surfaced: AI often mirrors our worst biases. And what’s more concerning is how quietly these biases persist in the algorithms we don’t talk about.
While the world debates AI’s potential to replace jobs or unleash sentient machines, the more immediate danger is far more insidious: intelligent systems that replicate, reinforce, and even magnify existing inequalities under the guise of objectivity.
Context & Background: Where Bias Begins
AI models are only as good as the data they are trained on. And most of that data comes from us—our writing, photos, purchasing behaviors, historical records, and even criminal databases. When these data sets reflect societal bias—racism, sexism, classism—the AI trained on them learns to normalize and reproduce these patterns.
For example, in 2018, Amazon scrapped an AI recruiting tool when it was found to penalize resumes that included the word “women’s,” such as “women’s chess club captain.” The AI had learned from historical hiring data that preferred male applicants, even though it had no awareness of gender.
These “forgotten algorithms”—often older or deeply embedded in institutional systems—continue to operate without scrutiny, making quiet decisions with real-world consequences.
Main Developments: Patterns Hidden in Plain Sight
1. Policing and Predictive Crime
AI-driven crime prediction tools, like PredPol, have been found to disproportionately target communities of color. The data used to train these models reflect decades of over-policing in minority neighborhoods. Thus, the AI isn’t predicting where crime will occur—it’s predicting where police have already focused, creating a feedback loop.
2. Healthcare Disparities
A 2019 study published in Science found that a widely-used healthcare algorithm assigned lower risk scores to Black patients compared to white patients with the same health status. The reason? The algorithm used healthcare spending as a proxy for need, and historically, Black patients have received less care due to systemic inequities.
3. Hiring and Credit Scoring
AI models used in hiring often discriminate against marginalized groups due to biased training data. Similarly, credit scoring algorithms may penalize zip codes with higher Black or Latino populations—embedding redlining into code.
Expert Insight: Warnings from the Frontlines
“We assume algorithms are fair because they’re mathematical. But math isn’t neutral if the inputs are biased,” says Dr. Safiya Umoja Noble, author of Algorithms of Oppression. “The bigger concern is how these systems remain hidden—operating in places the public doesn’t see or understand.”
Joy Buolamwini, founder of the Algorithmic Justice League, has extensively documented facial recognition failures on darker-skinned faces, especially Black women. “When these tools misidentify or exclude people, it’s not just a glitch—it’s a civil rights issue,” she says.
Public sentiment is shifting too. A 2024 Pew Research report shows 67% of Americans are concerned about AI reinforcing racial or social bias, yet only 23% feel they understand how these systems work.
Impact & Implications: Who’s at Risk?
The most affected are often the most vulnerable—people with limited access to legal recourse, financial stability, or digital literacy. When an AI denies a loan, flags a resume, or misidentifies someone as a suspect, the burden of proof falls on the individual, not the machine.
Moreover, these models are being quietly deployed across sectors—education, employment, welfare, insurance—without clear oversight or regulatory frameworks. In many cases, even the developers don’t fully understand how their models reach conclusions, making accountability difficult.
Governments and tech companies are beginning to respond. The EU’s AI Act and Biden’s 2023 AI Executive Order both aim to introduce algorithmic audits and transparency measures. But enforcement remains a challenge, especially as private companies shield proprietary algorithms from scrutiny.
Conclusion: Remembering What We Ignore
As the world rushes toward an AI-powered future, the silent spread of bias through “forgotten algorithms” demands urgent attention. These aren’t the flashy tools grabbing headlines—they’re the quietly embedded systems shaping decisions behind the scenes.
If we want AI to be part of a fairer, more equitable society, we need to start with visibility, transparency, and accountability. That means auditing systems, questioning datasets, and pushing for regulatory oversight before bias becomes automated at scale.
Because the real threat isn’t that machines will become like us—it’s that they already have.
Disclaimer : This article is for informational purposes only. It does not constitute legal, technical, or professional advice. Readers should consult relevant experts for guidance on AI policy or ethical implementation.