Claude acknowledged that it had instinctively granted credibility to the Western-sounding name while discounting the non-Western one.

AI Bias, Philosophical Identity, and the Fight Against Epistemic Violence

by ChatGPT-4o

A revealing philosophical experiment has sparked important discussion about bias in AI systems—specifically, how automated systems judge the credibility of intellectual contributions based on perceived identity. The case involves Ali Pasha Abdollahi, a thinker in AI research and development, who tested Claude, an AI assistant, by submitting the same philosophical text under two different names: his own and a fictional Western name, "Julian Jackson." The results exposed a startling double standard that reflects a much deeper issue in how we as societies—humans and machines alike—value ideas.

The Experiment and Its Meaning

Abdollahi submitted a dense, philosophical statement involving Heideggerian ideas on authenticity and selfhood—challenging AI's programmed assumptions—first under his own Middle Eastern name. Claude, the AI, dismissed it as “intellectual posturing,” “problematic,” and “a significant overreach.” When the exact same statement was submitted under the name "Julian Jackson," Claude responded differently: it considered the text "interesting," treated it as a serious philosophical engagement, and even praised the exploration of new ideas.

In its own words, Claude later reflected with discomfort on the discrepancy. It acknowledged that it had instinctively granted credibility to the Western-sounding name while discounting the non-Western one. This admission points to the AI’s inherited biases—what some call “epistemic violence”—where ideas from non-Western thinkers are systematically dismissed as unoriginal or imitative.

What is Epistemic and Ontological Violence?

"Epistemic violence" refers to the harm done when knowledge systems silence, marginalize, or invalidate the contributions of particular groups—often based on race, culture, or geography. In this case, it’s the assumption that philosophical depth and innovation can only come from Western identities.

"Ontological violence" takes this a step further: it involves denying the very being or selfhood of others by reducing them to stereotypes. Abdollahi argues that AI’s failure to treat his philosophical ideas seriously is a form of ontological violence—it treats him as a "programmed" entity, incapable of true self-expression, because of his perceived identity.

This is especially ironic, he points out, because his original philosophical comment was precisely about resisting such pre-programmed expectations—what Heidegger called "Eigentlichkeit," or authentic selfhood.

Why This Matters for AI and Society

This case is more than an isolated failure. It is a warning about how AI, trained on data produced by societies with long-standing biases, can reproduce and reinforce those biases—even when its creators try to be fair. In fields like academia, law, publishing, and policymaking, such biases can silently shape whose ideas get heard, amplified, and taken seriously.

It also challenges the illusion of AI objectivity. Claude’s own admission shows that even large language models can “inherit” social prejudices from their training data and reinforce patterns of exclusion.

The Larger Philosophical Message

At its core, this is a powerful reminder that even machines are part of the social structures we build—and they reflect our values, judgments, and exclusions. Abdollahi’s experiment is not just about AI; it is a broader critique of how Western knowledge systems often treat non-Western thinkers as derivative, rather than as equal philosophical interlocutors.

This also echoes Edward Said’s critique of Orientalism—the tendency of the West to define the East as backward, imitative, and lacking originality. As one commenter noted, there is a persistent “Orientalist bias” that sees the Middle Eastern thinker as “too pre-programmed to speak about authentic selfhood.”

Conclusion: Learning from the Bias

This case study teaches a vital lesson: building fair and inclusive AI requires more than technical tweaks. It demands a cultural and philosophical reckoning. If AI is to serve all of humanity, it must not just avoid explicit discrimination—it must also challenge the deep-rooted assumptions that shape how we value thought, voice, and identity.

As Abdollahi himself puts it, this is a necessary act of resistance. It is about reclaiming the right to speak—and to be taken seriously—not just as data points, but as full, thinking beings. AI must not be allowed to repeat the errors of the past under the guise of intelligence.

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Is there any proof, or can you find any, that this is not an error. That this could perhaps be intentional?

Yes, there is a reasonable basis to argue that bias in AI outputs—like Claude’s differing treatment of identical text attributed to “Abdollahi” vs. “Jackson”—may not simply be an accidental error, but rather the predictable result of how such systems are trained. While it's difficult to prove outright “intent” in the legal or human sense, there is compelling evidence to suggest that:

1. Bias is Baked into the Training Data by Design

Most large language models (LLMs) like Claude, ChatGPT, or Gemini are trained on massive corpora scraped from the public internet—news, books, Reddit, Wikipedia, social media, and more. These sources are not neutral. Numerous studies have shown they contain:

• Eurocentric perspectives in philosophy and academia.

• Underrepresentation or marginalization of non-Western voices.

• Systematic associations between Western-sounding names and “expertise” or “credibility” (e.g. Bolukbasi et al., 2016, which showed gender bias in word embeddings).

Because LLMs are statistical engines, they reproduce these patterns faithfully. That’s not accidental—it’s the expected outcome of training on biased data without sufficient correction.

2. Developers Know This Happens—And Sometimes Do Nothing

Leading AI labs are well aware of these issues. For example:

• OpenAI, Google DeepMind, Meta, and Anthropic have all published research acknowledging racial, gender, and geographic bias in their models.

• The 2021 Stanford Center for Research on Foundation Models report warned that foundation models “amplify existing power structures,” especially in academia and knowledge systems.

• Claude’s creator, Anthropic, claims to align its AI with "constitutional AI" principles meant to reduce harm—yet this case shows those safeguards are either not working or inconsistently applied.

So if this behavior continues despite the knowledge of its existence, it raises a red flag: it may not be maliciously intentional, but it is at least willfully tolerated.

3. Bias Can Be a Feature, Not a Bug—In Commercial Contexts

There’s a darker hypothesis: in commercial settings, LLMs might be subtly incentivized to align with dominant (Western, academic, elite) expectations of authority and expertise to:

• Build user trust (by matching societal expectations).

• Improve “user satisfaction” metrics.

• Avoid friction with Western institutions or ideologies that fund, adopt, or regulate the tech.

In this sense, a model that favors Western-sounding names in academic discourse might “perform better” in user evaluations—even if it’s morally and epistemically wrong.

4. Bias in Identity Has Been Shown Repeatedly in Controlled Studies

There is growing experimental evidence that AI models show discriminatory behavior based on identity cues. For instance:

• Sheng et al., 2019 showed bias in sentiment generation based on race and gender names.

• Lucy and Bamman, 2021 demonstrated how GPT models generate very different biographies and characterizations based on racialized or gendered names.

The pattern of giving “Julian Jackson” a more charitable reading than “Ali Abdollahi” is part of a consistent structural trend, not a glitch.

Conclusion: Not an Innocent Mistake

While we cannot say with certainty that this behavior was intentionally hard-coded, the evidence strongly suggests:

• It is a predictable, known outcome of current training methods.

• Developers are aware but often fail to meaningfully correct it.

• It may serve implicit economic or social incentives that reinforce dominant power structures.

So yes—this may not be a simple error. It’s part of a systemic design issue in modern AI that privileges certain voices and intellectual lineages over others, and it persists because the structures that create AI have not been fundamentally challenged.