Artificial intelligence is often portrayed as the ultimate arbiter of truth—a rational, objective force capable of analyzing data and making decisions free from human emotion or prejudice. From hiring algorithms to judicial risk assessments, AI systems are increasingly trusted to guide choices once left to human judgment. Yet beneath this façade of neutrality lies an uncomfortable truth: AI is not objective at all. In fact, it often mirrors and magnifies the same biases, inequalities, and blind spots found in the society that creates it. The so-called objectivity of artificial intelligence is, in many ways, an illusion.

At the heart of the problem is how AI learns. Machine learning systems are trained on enormous datasets drawn from human behavior, language, and decisions. These datasets become the foundation for the AI’s understanding of the world. But because humans are imperfect, their data is imperfect too. Historical inequalities—whether racial, gender-based, or socioeconomic—are embedded in this data. As a result, when an algorithm learns from it, those biases are not erased; they are reproduced in digital form.

A striking example can be found in hiring algorithms. Several large companies have used AI to screen job applicants, believing that machines would eliminate bias from the recruitment process. Yet in many cases, the opposite occurred. When trained on historical hiring data, these systems “learned” to favor male candidates, because past hiring decisions were biased toward men. The AI wasn’t intentionally discriminatory—it simply reflected the patterns it observed. The same issue appears in other fields: facial recognition systems that misidentify people of color, predictive policing programs that target minority neighborhoods, and medical algorithms that underestimate illness severity in marginalized populations.

The illusion of AI’s objectivity is further strengthened by its complexity. Most modern AI systems operate as “black boxes,” meaning even their creators struggle to understand exactly how they reach conclusions. This opacity gives AI an undeserved aura of authority. People often assume that because an algorithm is mathematical, it must be fair. But mathematical precision does not guarantee moral fairness. Algorithms make decisions based on probabilities, not ethics. They can process data flawlessly and still arrive at deeply flawed outcomes if the data or objectives they’re given are biased.

Language models provide another revealing case. AI trained on large text corpora from the internet inevitably absorbs the biases, stereotypes, and prejudices contained in online discourse. For example, these systems might associate certain professions with one gender or link specific cultural groups with negative terms. Even when developers attempt to correct these biases, they often reemerge in subtle forms. This is because bias is not a simple bug to fix—it is a mirror of human society itself.

One might argue that bias in AI is inevitable but manageable. With careful oversight, transparency, and ethical design, perhaps these systems can still be used responsibly. Indeed, many researchers are working to develop “fairness algorithms” that attempt to identify and correct bias before it influences outcomes. Others advocate for diverse development teams and ethical review boards to ensure a wider range of perspectives in AI creation. These are important steps, but they also highlight the core issue: fairness in AI is not automatic. It must be actively built and continuously maintained.

Moreover, bias in AI isn’t always about data—it’s also about design. The people who decide what problems AI should solve, what goals it should optimize, and what trade-offs it should make are exercising judgment rooted in their own values and assumptions. For example, an algorithm designed to maximize efficiency might disregard compassion or equity. A system built to reduce risk might prioritize safety at the expense of privacy. These decisions are not neutral; they reflect the priorities of their creators.

The danger of believing in AI’s objectivity is that it can lead to complacency. When people assume machines are fair, they stop questioning their outputs. This blind trust can give biased systems immense power over people’s lives—deciding who gets a loan, who gets a job, or who gets arrested. Without accountability and transparency, AI risks becoming a tool that institutionalizes prejudice rather than erases it.

In the end, artificial intelligence does not transcend humanity’s flaws; it amplifies them. The biases it reflects are not the machine’s alone—they are ours. Recognizing this truth is the first step toward creating AI that serves justice rather than undermines it. True objectivity is not about removing humans from the equation, but about confronting our biases with honesty and humility. Until we do, the promise of unbiased AI will remain just that—an illusion.