The Forgotten Algorithms: How AI Models Mirror Human Bias in Ways We Don’t Talk About
AI algorithms often reflect human bias—but not always in obvious ways. This deep dive explores the overlooked ways AI inherits and reinforces societal inequalities.
Introduction: When Machines Inherit Our Blind Spots
In 2018, a major tech company quietly retired an AI recruiting tool after discovering it systematically penalized resumes that included the word “women’s.” The problem wasn’t in the code—it was in the data. The algorithm had learned from a decade of biased hiring practices. While the tool was decommissioned, it sparked a deeper, more uncomfortable question: If AI is trained on human behavior, how much of our prejudice do we unknowingly encode?
We often talk about the promise of artificial intelligence, but rarely do we discuss its shadows. The algorithms we entrust with decisions—from hiring and lending to policing and medical care—are not as objective as we like to believe. In fact, they often replicate and amplify the very biases we hope technology will overcome.
Context & Background: A Legacy of Biased Inputs
The core of the problem lies in how machine learning models are trained. These models “learn” from historical data, making predictions or decisions based on patterns in that data. But history is not neutral. It’s a reflection of society—its prejudices, its inequities, its blind spots.
Take facial recognition, for example. In 2019, a landmark study by MIT’s Joy Buolamwini found that commercial facial analysis systems misidentified Black women at a rate of up to 35% compared to less than 1% for white men. Why? Because the datasets used to train these systems were predominantly composed of lighter-skinned individuals.
Similarly, in the financial sector, credit-scoring algorithms have been found to give lower scores to applicants from minority neighborhoods—even when their financial histories were nearly identical to those of white applicants. The algorithm didn’t “see race” explicitly, but it saw ZIP codes, employment history, and other proxies that serve as stand-ins for systemic inequality.
Main Developments: The Quiet Spread of Invisible Bias
What’s often overlooked is how these biases become self-reinforcing. When an algorithm denies someone a loan based on biased criteria, that decision becomes part of the data used to train future models. Over time, the algorithm doesn’t just reflect societal inequality—it helps entrench it.
A 2023 report from the Algorithmic Justice League warns of a “feedback loop of injustice” where biased outputs feed back into the system, exacerbating disparities. And because these decisions are automated, they’re harder to challenge. Unlike a human decision-maker, an AI model doesn’t offer explanations, only outcomes.
In the criminal justice system, predictive policing tools have drawn criticism for over-policing minority communities. These systems often use arrest records to predict crime hotspots. But arrests are not objective indicators of crime—they’re influenced by policing practices, which historically have targeted certain groups disproportionately.
Expert Insight & Public Reaction
“AI doesn’t create bias out of thin air,” says Dr. Timnit Gebru, an AI ethics researcher and former co-lead of Google’s Ethical AI team. “It amplifies the bias that already exists in the world. The danger is that we treat algorithmic decisions as more trustworthy because they seem scientific.”
Public sentiment is slowly catching up. Recent Pew Research shows that 66% of Americans are concerned about algorithmic bias in hiring, law enforcement, and healthcare. Yet awareness does not always translate into policy. There’s still a significant gap in transparency: companies often guard their training data and algorithmic design as proprietary secrets.
Even well-intentioned interventions can fall short. Tools designed to “de-bias” models—like fairness constraints or synthetic training data—can only go so far if the broader system (legal, social, institutional) remains unchanged.
Impact & Implications: Who’s Affected and What’s at Stake?
The impact of these forgotten algorithms is not abstract—it affects real people in life-altering ways. A biased healthcare algorithm can lead to poorer treatment for Black patients. A skewed resume screener can prevent women or disabled individuals from being hired. An opaque credit-scoring system can block families from buying homes or starting businesses.
Beyond individual harms, there’s a larger risk: the erosion of trust. If people begin to believe that AI systems are rigged—or worse, if they never realize it—they may stop engaging altogether or push back in damaging ways.
There’s also an international dimension. AI models trained in the Global North are increasingly being deployed in the Global South, where social contexts are vastly different. This creates new layers of misalignment and potential harm that regulators have yet to address.
Conclusion: Toward Ethical, Accountable AI
The story of AI is not just about innovation—it’s about reflection. The forgotten algorithms we rarely question have enormous influence, quietly shaping the world around us. If left unchecked, they risk deepening the very injustices they were meant to solve.
The solution isn’t to abandon AI, but to approach it with humility, transparency, and rigorous ethical oversight. That means diversifying the teams building these models, opening up training datasets for scrutiny, and holding companies accountable for algorithmic harm.
We cannot fix what we do not see. And when it comes to AI, it’s time we start looking closer.
Disclaimer : This article is for informational purposes only and does not constitute legal, ethical, or technological advice. The views expressed are based on publicly available research and expert commentary.