How can we ensure it reflects the full spectrum of human culture, not just the sliver of it that is most visible online?
We’ve all seen the headlines about AI getting things comically wrong—generating historically inaccurate images of popes or confidently “hallucinating” facts that never were. While these surface-level glitches are easy to spot and dismiss, the real risks of artificial intelligence are woven far more deeply into its fundamental architecture.
These systems are rapidly becoming what researcher Declan Humphreys calls "epistemic tools"—a primary way we find things out, answer questions, and understand the world. But this new window to reality isn't a neutral pane of glass. It has a built-in tint, a distortion that subtly shapes what we see. Here are four of the most surprising and impactful ways AI's hidden architecture is shaping our reality.
1. The Very Process That Makes AI “Safe” Is Also What Makes It Biased
To prevent AI models from generating dangerous or toxic content, developers put them through a "safety training" process called Reinforcement Learning from Human Feedback (RLHF). In simple terms, humans—often paid crowd-workers—provide feedback, teaching the model to produce helpful and harmless responses.
But here’s the counter-intuitive finding: this alignment process doesn't align the AI with universal "human values." Instead, it aligns the model to the specific preferences of a small, unrepresentative group of model designers and labellers. This isn't just about the labellers' personal opinions. Research shows the bias is structural: labellers are often paid workers in precarious "digital sweatshop" conditions, selected and guided to conform to the organization's specific values and guidelines. This can instill what researchers call a "collective bias" or "bias of crowds," causing the AI to reflect the specific moral and political leanings of its creators.
"large-scale generative models hold the possibility of magnifying and minimizing different parts of human culture in unpredictable and opaque ways, which could have broad downstream influences"
2. AI “Thinks in English”—And It Can Cost Non-English Speakers Up to 7 Times More
Many multilingual users have anecdotally noticed that AI responses in their native language can feel "janky" or sound like an "obvious English calque," as one Reddit user put it. It turns out there is a deep, structural reason for this: tokenization.
Tokenization is how an AI breaks down text into smaller pieces, or "tokens," to process it. Research into this process reveals a stark disparity: it is highly inefficient for many non-English languages. This phenomenon, known in the research as "token inflation" or "excessive subword fragmentation," means some languages are broken into far more pieces than others. One study found that languages using the Myanmar script require nearly 7 times more tokens than languages using the Latin script to represent an equivalent amount of text.
This isn't just a technical curiosity; it has a significant real-world consequence. Because many AI services are priced based on the number of tokens used, this "infrastructure bias" creates a hidden financial barrier. It makes AI tools significantly more expensive for speakers of underrepresented languages, effectively putting a tax on communication that isn't in English.
3. AI Gazes at the World Through a “Silicon Gaze,” Systematically Favoring the West
Researchers from the Oxford Internet Institute have coined the term “silicon gaze” to describe how generative AI reproduces long-standing global inequalities. Because Large Language Models (LLMs) learn from the vast corpus of the internet—a dataset overwhelmingly dominated by English-language content from wealthier, Western nations—their entire worldview is skewed.
A study analyzing over 20 million ChatGPT queries found this bias in action. When asked subjective questions like “Where are people smarter?” or “Which country is safer?”, the model systematically ranked higher-income Western regions more favorably. Meanwhile, large parts of Africa, the Middle East, and Latin America consistently ranked at the bottom. As AI is increasingly integrated into decision-making tools for everything from business to public services, it risks not just mirroring but actively reinforcing these exact global inequalities. To see this "silicon gaze" in action, the research team created a public website, inequalities.ai, where you can explore how ChatGPT ranks your own country or city.
4. An AI's "Cultural IQ" Depends on a Language's Digital Footprint
A model's ability to accurately reflect the societal values of a specific country—its "cultural fidelity"—is directly tied to a simple metric: the amount of online data available in that country's language. A recent study quantified this relationship for GPT-4-turbo, finding a staggering 72% correlation between a language's digital footprint and the model's accuracy.
This shows a system learning its values from what's most visible online. The good news is that AI companies are aware of this problem and are actively working on it. With the newer GPT-4o model, for example, that correlation dropped significantly to 44%, a sign of genuine progress. However, despite this improvement, the fundamental disparity remains. The same study found that the error rate for representing societal values is still more than five times higher for languages with the lowest digital resources compared to those with the highest. This dynamic threatens to create a new, more severe "digital divide," where assimilation toward dominant online languages—a dynamic some researchers call a new form of digital colonization—becomes a prerequisite for digital participation.
Bottom Line: An Algorithm's Worldview
The biases shaping our AI tools are not simple bugs that can be easily patched. From the "bias of crowds" instilled during safety training and the infrastructural tax of tokenization, to the "silicon gaze" that centers the West and the direct link between a language's "cultural IQ" and its digital footprint, these systems are a product of the very human inequalities they learn from.
As we increasingly rely on AI to be our window to the world, how can we ensure it reflects the full spectrum of human culture, not just the sliver of it that is most visible online?
About the Writer
Jenny, the tech wiz behind Jenny's Online Blog, loves diving deep into the latest technology trends, uncovering hidden gems in the gaming world, and analyzing the newest movies. When she's not glued to her screen, you might find her tinkering with gadgets or obsessing over the latest sci-fi release.What do you think of this blog? Write down at the COMMENT section below.
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