Large Language Models inherit the biases of human civilization while claiming objectivity. But should they be neutral arbiters or faithful mirrors of human complexity? The answer reveals fundamental questions about truth, representation, and the nature of knowledge itself.
When we ask an LLM about controversial topics, we expect balanced, objective responses. When it discusses historical events, we want factual accuracy. When it addresses social issues, we demand fairness. But what if the very notion of an “unbiased” LLM is not just impossible but fundamentally misguided?
The question of bias in Large Language Models reveals a deeper paradox about knowledge, representation, and truth. These systems are trained on the collective output of human civilization—our books, articles, websites, and conversations. They inherit not just our facts but our perspectives, prejudices, and partial truths. Yet we expect them to somehow transcend the biases that permeate their training data and deliver pure, objective knowledge.
This expectation raises profound questions: Should LLMs strive to be neutral arbiters that somehow stand above human bias? Or should they function more like dictionaries—comprehensive repositories that capture the full spectrum of human thought, including its contradictions and prejudices? The answer isn’t just technical; it’s philosophical.
The dream of neutral AI rests on a fundamental misconception: that bias is a bug to be fixed rather than an inherent feature of how knowledge is created, transmitted, and understood. Every piece of text that trains an LLM was written by humans embedded in particular cultures, historical moments, and social positions. These authors didn’t just record neutral facts; they interpreted, emphasized, and framed information according to their own understanding and values.
Consider how different sources describe the same historical event. Western textbooks and Chinese textbooks don’t just present different facts about the Opium Wars—they operate from entirely different frameworks about colonialism, sovereignty, and historical justice. Both perspectives contain truths, but neither is neutral.
When an LLM synthesizes thousands of such sources, whose perspective should dominate? The most common viewpoint? The most academically credible? The most recent? Each choice embeds particular biases while claiming to eliminate them.
There’s a seductive belief that aggregating many biased sources somehow produces unbiased results—that the prejudices cancel each other out, leaving pure truth. But bias doesn’t work like noise in a signal. It’s more like spice in cooking: you can’t remove the salt by adding more pepper.
When an LLM encounters a thousand texts that assume certain economic systems are natural and inevitable, and ten texts that question these assumptions, the model learns to reproduce the majority perspective as objective truth. The aggregation process doesn’t eliminate bias; it systematically privileges the most represented viewpoints.
Even determining what constitutes “bias” requires a baseline of supposed objectivity. But who establishes this baseline? When we say an LLM is biased toward Western perspectives, we’re implicitly claiming to know what a non-Western perspective looks like. When we identify gender bias, we’re assuming we understand what gender-neutral language should be.
These assessments themselves reflect particular viewpoints about what neutrality means and what perspectives deserve representation. The very frameworks we use to identify bias are products of specific intellectual traditions and cultural assumptions.
The training data that shapes LLMs offers a fascinating window into this bias paradox. This data represents the largest corpus of human expression ever assembled—millions of books, billions of web pages, countless conversations. In one sense, it’s the most comprehensive representation of human knowledge and opinion ever created. In another sense, it’s profoundly skewed.
The internet, which provides much of LLM training data, isn’t a neutral sample of human thought. It overrepresents:
Entire civilizations, cultures, and ways of understanding the world are systematically underrepresented or entirely absent. When an LLM “knows” more about Silicon Valley startups than indigenous farming practices, is this bias or simply a reflection of what humans have chosen to digitize and publish?
Even within digital spaces, some voices are amplified far beyond others. A single prolific blogger might contribute more training data than thousands of occasional writers. Academic institutions produce vastly more indexed content than community organizations. Corporate communications outweigh grassroots perspectives.
This creates a distortion where the most prolific voices become the most “true” from the model’s perspective. The LLM doesn’t just learn what people think; it learns to weight opinions according to their digital footprint rather than their validity or representativeness.
Training data also creates temporal distortions. Recent content dominates older material, not because it’s more accurate but because it’s more abundant and accessible. This means LLMs might be better at representing contemporary American perspectives than classical philosophical traditions, despite the latter’s arguably greater depth and influence.
The result is a strange temporal flattening where the immediate past becomes disproportionately influential in shaping how the model understands enduring human questions.
Faced with bias accusations, LLM developers often retreat to what we might call the “dictionary defense”: these systems are simply comprehensive repositories of human knowledge, like dictionaries that include offensive terms because they exist in language, not because the dictionary endorses them.
This analogy is both compelling and problematic. Dictionaries do include slurs, controversial definitions, and contested meanings. A good dictionary captures how language is actually used, not how we might wish it were used. Should LLMs function similarly—as neutral repositories that preserve the full spectrum of human thought, including its ugly parts?
Under this view, an unbiased LLM would be one that accurately represents the distribution of human opinions as they actually exist, not as we think they should exist. If 60% of training data expresses certain views about economic systems, the model should reflect that distribution. If historical sources predominantly represent colonial perspectives, the model should acknowledge this while faithfully representing those perspectives.
This approach treats bias not as error but as information. The model’s job isn’t to correct human prejudices but to accurately represent them, allowing users to understand and critically evaluate the perspectives they encounter.
But unlike dictionaries, LLMs don’t just store information—they actively generate new text that synthesizes and extends their training data. When a dictionary includes a slur, it clearly marks it as offensive and provides context. When an LLM generates text, it might reproduce harmful stereotypes without any such framing.
This generative capacity means LLMs can’t maintain the neutral stance of reference works. They must make choices about how to synthesize conflicting viewpoints, which perspectives to privilege, and how to handle sensitive topics. These choices inevitably embed values and judgments.
The dictionary defense also risks creating false equivalences between well-supported and fringe viewpoints. If training data includes both climate science and climate denial, should an LLM present both as equally valid? If sources contain both historical scholarship and conspiracy theories, should they receive equal weight?
A truly neutral repository might need to preserve these distinctions rather than flattening all perspectives into equivalent “opinions.” But making such distinctions requires judgments about credibility, evidence, and truth—precisely the kinds of judgments that introduce bias.
Perhaps the most challenging aspect of LLM bias involves whose perspectives are represented and how their weight is determined. The training data doesn’t just contain biases; it embeds particular power structures about whose voices matter and whose can be ignored.
Academic publications carry more weight in training data than personal blogs. English-language sources dominate multilingual content. Wealthy nations’ perspectives overshadow those of developing countries. These aren’t accidental technical limitations; they reflect deeper inequalities in who gets to participate in global knowledge creation.
When an LLM reproduces these patterns, is it being biased or accurate? It’s accurately representing a world where some voices have more platforms and resources than others. But it’s also perpetuating these inequalities by treating them as natural rather than constructed.
Training data also embeds assumptions about expertise and authority. Scientific journals outweigh personal experience. Institutional perspectives dominate grassroots knowledge. Formal education credentials overshadow experiential wisdom.
These hierarchies aren’t necessarily wrong, but they’re not neutral either. They reflect particular beliefs about what kinds of knowledge count and who gets to be considered an authority. An LLM trained on this data will reproduce these hierarchies, presenting certain types of sources and perspectives as more credible than others.
Some human experiences are extensively documented while others remain largely invisible in digital spaces. Middle-class professional life is exhaustively represented across blogs, articles, and social media. Rural, working-class, or marginalized experiences appear far less frequently in the kinds of sources that train LLMs.
This creates a distortion where the most documented life experiences become the model’s default understanding of what’s normal or typical. The LLM isn’t just biased toward certain demographics; it’s biased toward the kinds of experiences that privileged demographics choose to write about.
The bias question in LLMs ultimately reveals fundamental tensions about the nature of knowledge, truth, and representation in democratic societies. These aren’t technical problems to be solved but philosophical dilemmas to be navigated.
Should LLMs strive to tell us what’s true or what people believe? These might be different things. If most historical sources represent colonial perspectives, should an LLM correct for this bias or faithfully represent how history was actually recorded?
If contemporary sources contain widespread misconceptions about scientific topics, should the model educate users or reflect the current state of public understanding? Each choice involves judgments about whether truth or representation takes priority.
Removing bias might benefit marginalized groups by eliminating harmful stereotypes and misconceptions. But it might also create a sanitized version of human knowledge that obscures important truths about how bias actually functions in society.
Understanding historical and contemporary prejudices might require encountering them, not having them filtered out. The question becomes whether protecting individuals from harmful content outweighs the collective benefit of understanding how bias operates.
Human perspectives on controversial topics continue evolving. What seems obviously biased today might have been considered objective truth a generation ago. Should LLMs reflect this evolution by weighting recent perspectives more heavily, or should they preserve the full historical spectrum of human thought?
This temporal question has no clear answer. Privileging contemporary views might eliminate historical biases but introduce new ones. Preserving all perspectives equally might perpetuate harmful ideas that society has moved beyond.
The questions remain open:
Can any system trained on human data transcend human limitations? Should it try to? If LLMs must be biased, whose biases should they reflect? How do we balance the goal of accurate representation against the need for harmful content filtering? What does it mean to be objective about subjective human experiences?
Perhaps the real question isn’t whether LLMs can be unbiased, but whether we can be honest about the biases they inevitably contain—and whether that honesty might be more valuable than the impossible dream of perfect neutrality.
AI Attribution: This article was written with the assistance of Claude, an AI assistant created by Anthropic, whose own biases undoubtedly influenced this exploration of bias itself.