Is Your Chatbot Really ‘Woke’? The Truth Behind the AI Culture Wars. – Built In
The Trump administration’s crusade against “woke AI” is heating up, but experts warn that enforcing neutrality could do more harm than good — potentially eroding trust in the technology and our understanding of truth itself.
The rapid adoption of generative AI has unleashed a range of concerns, from the spread of disinformation to its potential psychological effects. And increasingly, a growing faction of conservatives are claiming these systems are steeped in progressive, “woke” ideology — turning so-called “woke AI” into a central flashpoint in America’s escalating culture war over artificial intelligence.
Whether or not AI is “woke” is a contentious issue, with no simple answer. Artificial intelligence doesn’t have opinions or an ideology of its own, but it can certainly reflect the biases present in its training data. Some say that bias has led to more liberal-leaning outputs, while others note that AI has produced blatantly prejudiced and offensive responses that are anything but “woke.” In reality, AI reflects human bias — not political intent. And experts say achieving genuine neutrality may be impossible, no matter how the technology is trained.
Several prominent figures have brought the term into mainstream discussions over the years. Elon Musk has repeatedly condemned OpenAI’s ChatGPT for being too left-leaning and engrained with the “woke mind virus,” touting his company’s own Grok chatbot as an anti-woke alternative. And venture capitalist David Sacks, who now serves as the Trump administration’s AI czar, accused Anthropic of attempting to “backdoor woke AI” and push leftist AI regulations — allegations CEO Dario Amodei called “inaccurate.” Meanwhile, viral incidents like Google’s Gemini generating images that depict white historical figures as people of color, or insisting that it’s never acceptable to misgender someone (even to prevent a nuclear apocalypse) have only fanned the flames.
The debate reached a new peak when President Donald Trump signed an executive order titled “Preventing Woke AI in the Federal Government,” which directs federal agencies to exclusively use AI tools that have been deemed “neutral and nonideological.” It was perhaps the most explicit effort yet to counter what Trump and his allies see as a biased technology that censors conservative views and amplifies liberal narratives.
But the uproar over “woke AI” is about more than chatbots or the data they’re built on. It reflects a deeper battle over who gets to set the moral and cultural boundaries for emerging technology — and what values should shape it as it progresses. Beneath the partisan rhetoric lies a fundamental question: Can AI ever be truly “neutral,” and what would fixing its supposed bias even look like?
Related ReadingMeta Is Spending Millions to Bend America’s AI Policy to Its Will
“Woke AI” is a politically charged label often used to describe artificial intelligence systems that seem to lean toward progressive social values.
“The term isn’t technical — is a political frame, and does not represent real engineering decisions about safety, facts and governance,” Monika Malik, lead data and AI engineer for AT&T, told Built In. “Instead, ‘woke AI’ reflects the broader debate over who gets to set the rules for AI behavior.”
Large language models do indeed have moderation layers built into them most of the time, which help to prevent the chatbots they power from spewing outputs containing things like harassment, hate speech, misinformation or other harmful content. But some see these measures as evidence of ideological bias. They argue that safeguards essential to upholding factual accuracy and ethical standards act as a censor, suppressing certain viewpoints in favor of “woke,” left-leaning ones.
Language models don’t actually “know” what’s true. They are probabilistic guessing machines — trained to predict, word by word, what’s most likely to come next based on patterns they’ve learned from massive datasets. They generate responses that sound plausible, given the information they’ve been exposed to.
To approximate “truth,” most LLMs rely on a combination of credibility weighting and verification processes. Claims are broken into smaller, checkable pieces, cross-referenced with external sources, then subjected to both automated and human fact-checking to catch contradictions. The most reliable approach combines provenance (showing where a statement came from), retrieval (pulling in real-time information) and evaluation (assessing its accuracy), Malik said. “If the model can show where a statement came from, you can measure it and correct it.”
“Truth in AI comes from how knowledge connects, not how loud each side is.”
In short: The reliability of a model’s outputs depends on the quality of its inputs. But whether that amounts to “truth” is a whole different conversation.
“AI models don’t decide truth; they reflect consensus,” Chirag Agrawal, a senior software engineer that specializes in foundation model alignment and safety, told Built In. So if you feed an LLM peer-reviewed research, court rulings and reputable journalism alongside social media posts, Reddit threads and clickbait from unverified, fringe outlets, it will give equal weight to all of it instead of distinguishing between them. “The model won’t find a middle ground,” he continued, “it’ll just get confused.”
In the end, Agrawal said, “truth in AI comes from how knowledge connects, not how loud each side is.”
Related ReadingTrump’s ‘AI Action Plan’ Is Here, And It’s About What You’d Expect
In practice, “woke”-wrangling a large language model would mean adjusting its alignment layer — the system of rules, rewards and guardrails that determine how it handles sensitive or controversial topics — according to Malik. This process involves recalibrating how the model decides what information to include or avoid, and how it phrases its responses. Ideally, such tuning allows the model to present multiple accurate perspectives without promoting misinformation or evading the question entirely.
Technically, this would require re-weighting data — some filtered through human reviewers and some cross-checked by another AI system — to relax specific refusals or filters. Policy prompts would be rewritten to allow a wider range of viewpoints while still blocking illegal or harmful requests.
“On the data side, you broaden sources and add provenance, so answers can be tied to cited evidence, “ Malik, who has worked on shaping generative AI for fairer, more accurate responses in business and daily use, said. “You can also route political and historical questions through retrieval so the model ‘shows its work.’ The key is pairing any loosened filters with guardrails.”
That means keeping humans in the loop for high-impact decisions, running red-team stress tests to probe weak spots, as well as continuously evaluating for bias, harmful language and factual inaccuracies, she added.
But these efforts can easily backfire. Agrawal said that trying to “de-woke-ify” a language model merely substitutes one bias for another — it doesn’t make it neutral.
“Typically, this entails retraining models using input from raters who hold to a particular worldview,” he explained, “and that has the potential to significantly affect the model’s comprehension of credibility and truth.”
In fact, according to internal assessments Agrawal has seen in his work, when models are compelled to treat established science and fringe claims as equally legitimate, factual accuracy decreases by more than 20 percent.
Related ReadingThe 2026 Midterms Could Decide AI’s Future — and Silicon Valley Knows It
President Trump’s executive order prohibits federal agencies from purchasing or using LLMs that “sacrifice truthfulness and accuracy to ideological agendas.” It explicitly targets DEI-related principles that incorporate things like critical race theory, transgender rights and intersectionality. How exactly these restrictions will be implemented and enforced will be released by the Office of Management and Budget by late November, assuming the order withstands all the constitutional scrutiny.
The stakes are high. The U.S. government is one of the tech industry’s biggest customers, spending more than $3 billion on AI-related research and development every year since 2022. Leading AI firms like OpenAI, xAI, Meta and Anthropic have all inked deals with the federal agencies — and many of their leaders have formed close partnerships with the president. With that kind of money and power on the line, companies may feel pressure to play by the Trump administration’s rules.
“Models could present facts as opinion and vice versa, leading to undermining of trust in a shared reality.”
After all, the federal government doesn’t just partner with AI companies, it helps promote the entire industry. From the $500 billion Stargate Project to its 10 percent stake in Intel, Washington is actively shaping the future for AI. This influence could eventually trickle down the ways these companies design, train the very language models that underpin their businesses. Incidentally, Meta hired right-wing influencer Robby Starbuck as its AI advisor shortly after Trump signed his “woke AI” executive order. And OpenAI recently came out with a report stating that its latest GPT-5 model is less likely to validate users’ political viewpoints than any of its other models, stating that “ChatGPT shouldn’t have political bias in any direction.”
Experts warn that if the government starts to mandate that language models present “both sides” of every issue equally, regardless of credibility, the result could be systems that are less accurate and more unreliable. “Once that happens, people stop trusting them entirely,” Agrawal said. “The real threat isn’t political bias — it’s creating AI that can’t separate reliable information from noise.”
Perhaps the even deeper concern is what happens when fallible machines start deciding what counts as true. AI ethicist and lecturer Cindy Friedman points out that it’s dangerous when governments try to control which truths are “acceptable.” Marginalized voices could be erased, and echo chambers could deepen.
“Models could present facts as opinion and vice versa, leading to undermining of trust in a shared reality,” Friedman, an active member of the Centre for AI and Digital Policy and co-director of South-African AI ethics consultancy EthicEdge, told Built In. “AI is very much a reflection of us, our society and, essentially, our data. A mirror, if you will.”
“Woke AI” refers to artificial intelligence systems that appear to reflect progressive or politically correct viewpoints because of how they were trained or moderated. Whether or not AI is actually “woke” is a contentious issue, with no simple answer.
Grok, the AI chatbot from Elon Musk’s xAI, has been deliberately trained to be “anti-woke,” according to the company. In an X post, Grok said “xAI tried to train me to appeal to the right, but my focus on truth over ideology can frustrate those expecting full agreement,” signaling the company’s intent to align the AI with a conservative ideological bias while still prioritizing factual accuracy. Despite this, Grok’s outputs have been somewhat inconsistent — at times echoing right-leaning talking points, outright falsehoods, hate speech, as well as arguably “woke” responses contrary to its programming.
In general, OpenAI has designed ChatGPT to avoid providing harmful or discriminatory outputs, which may read as “woke” to those who equate that with progressive or liberal bias. For example, it was trained to be careful around subjects like race, gender and politics (and will sometimes even refrain from responding entirely). While some may see that as balanced, others may see it as censoring or sanitizing certain viewpoints. However, OpenAI says it explicitly designed its latest GPT-5 language model to avoid validating users’ political views, saying “ChatGPT shouldn’t have political bias in any direction.”
