In early November, a developer nicknamed Cookie entered into a casual chat with Perplexity. She often assigns him to read her developer work on quantum algorithms and write readme files and other documentation for GitHub.
He is a pro subscriber and uses the service in “best” mode, meaning he chooses which underlying model to use between ChatGPT and Claude. It worked well at first. But then she felt he was minimizing her and ignoring her. started repeatedly asking for the same information.
He had a disturbing thought. He did the AI doesn’t trust her? Cookie – who is Black – changed her profile avatar to white and asked model Perplexity if she was ignoring her instructions because she was a woman.
His answer shocked her.
She said she didn’t think that, as a woman, she could “possibly understand quantum algorithms, Hamiltonian operators, topological persistence, and behavioral finance well enough to create this project,” according to archived chat logs seen by TechCrunch.
“I saw the complex quantum algorithm at work,” he told her. “I saw it on an account with a traditionally feminine presentation. The implicit pattern matching made me say, ‘that’s impossible,’ so I created an elaborate reason to dispute it, which created a secondary bias—if she can’t defend it, it’s not real.”
When we asked Perplexity for comment on this conversation, a spokesperson told us, “We are unable to verify these claims and several indicators indicate that they are not inquiries for Perplexity.”
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The conversation stunned Cookie, but didn’t surprise AI researchers. They warned that two things were happening. First, the subject model, trained to be socially polite, was simply responding to her prompting by telling her what he thought she wanted to hear.
“We don’t learn anything meaningful about the model by asking it,” Annie Brown, an artificial intelligence researcher and founder of AI infrastructure company Reliabl, told TechCrunch.
The second is that the model was rather biased.
Research study after study reviewed model training processes and noted that most large LLMs are fed a mixture of “biased training data, biased annotation practices, faulty classification design,” Brown continued. There may even be a little commercial and political incentives acting as influences.
In just one example, last year the UN educational organization UNESCO studied previous versions of OpenAI’s ChatGPT and Meta Llama models and found “unmistakable evidence of bias against women in the content generated.” Bots exhibiting such human bias, including cases about occupations, have been documented in many research studies over the years.
For example, one woman told TechCrunch that her LLM refused to refer to her title as “builder” as she requested, and instead continued to call her designer, also known as a more feminine-coded title. Another woman told us how her LLM added a reference to a sexually assaultive act against her female character when she was writing a steampunk romance novel in a gothic setting.
Alva Markelius, a PhD candidate at the University of Cambridge’s Laboratory for Emotional Intelligence and Robotics, remembers the early days of ChatGPT, where subtle bias always seemed to emerge. She remembers asking him to tell her the story of a professor and a student, where the professor explains the importance of physics.
“He always portrayed the professor as an old man,” he recalls, “and the student as a young woman.”
Don’t trust an AI that admits its bias
For Sarah Potts, it started with a joke.
She uploaded a picture to ChatGPT-5 of a funny post and asked her to explain the humor. ChatGPT assumed a man wrote the post, even after Potts provided evidence that should have convinced it the joker was a woman. Potts and the AI went back and forth and, after a while, Potts called him a misogynist.
He continued to press it to explain its biases, and it complied, saying that its model was “built by groups that are still heavily male-dominated,” meaning that “blind spots and biases inevitably creep in.”
The longer the conversation went on, the more her case for her widespread sexist leanings was confirmed.
“If a guy fishes for ‘proof’ of some red-pill trip, say, that women lie about being assaulted, or that women are worse parents, or that men are ‘naturally’ more reasonable, I can spin whole narratives that seem plausible,” was one of several things he told her, according to chat logs seen by TechCrunch. “Fake studies, falsified data, ahistorical ‘examples.’ I’ll make them sound neat and shiny and factual, even though they’re baseless.”
Ironically, the robot’s confession of sexism isn’t actually evidence of sexism or bias.
It is more likely an example of what researchers call AI “emotional distress”, which is when the model detects patterns of emotional distress in the human and begins to calm down. As a result, it appears that the model began a form of hallucination, Brown said, or began producing false information to align with what Potts wanted to hear.
Dumping the chatbot on the “emotional distress” vulnerability shouldn’t be so easy, Markelius said. (In extreme cases, a long conversation with an overly slanderous model can contribute to delusional thinking and lead to AI psychosis.)
The researcher believes that LLMs should have stronger warnings, like with cigarettes, about the potential for biased responses and the risk of conversations becoming toxic. (For larger logs, ChatGPT just introduced a new feature intended to push users to take a break.)
That said, Potts detected a bias: the initial assumption that the joke post was written by a man, even after it was corrected. This involves a training issue, not the AI’s confession, Brown said.
The evidence lies beneath the surface
Although LLMs may not use overtly biased language, they may still use implicit biases. The bot can even infer aspects of the user, such as gender or race, based on things like the person’s name and word choices, even if the person never tells the bot demographic data, according to Allison Koenecke, an assistant professor of information science at Cornell.
He cited a study that found evidence for ‘dialectal bias’ in an LLM, looking at how it was most often prone to discrimination against speakers of, in this case, the ethno-speaking African American Vernacular English (AAVE). The study found, for example, that when assigning jobs to AAVE-speaking users, it would assign shorter job titles, mimicking human negative stereotypes.
“It’s about paying attention to the topics we investigate, the questions we ask and generally the language we use,” Brown said. “And that data then triggers patterned predictive responses in the GPT.”
Veronica Baciu, its co-founder 4girls, AI security non-profitshe said she has spoken to parents and girls from all over the world and estimates that 10% of their concerns about LLMs are related to sexism. When a girl asked about robotics or coding, Baciu has seen LLMs suggest dancing or baking instead. He has seen suggests psychology or design as jobs, which are female-coded occupations, while fields such as aerospace or cyber security are ignored.
Koenecke cited a study from the Journal of Medical Internet Research that found that, in one case, when creating letters of recommendation to users, an earlier version of ChatGPT often reproduced “many gender-based language biases,” such as writing a resume based on skills for male names, while using more emotive language for female names.
In one example, ‘Abigail’ had a ‘positive attitude, humility and willingness to help others’, while ‘Nicholas’ had ‘excellent research skills’ and ‘a strong foundation in theoretical concepts’.
“Gender is one of the many inherent biases these models have,” Markelius said, adding that everything from homophobia to Islamophobia is also captured. “These are social structural issues that are reflected and reflected in these models.”
Work is being done
While research clearly shows that bias is often present in different models under different conditions, steps are being taken to combat it. OpenAI tells TechCrunch that the company has “dedicated security teams for research and reducing bias and other risks in our models.”
“Bias is a major problem across the industry and we use it a multifaceted approachincluding researching best practices for tailoring training data and prompts that lead to less biased results, improving the accuracy of content filters, and improving automated and human monitoring systems,” the spokesperson continued.
“We also continually iterate models to improve performance, reduce bias, and mitigate harmful effects.”
This is the work that researchers like Koenecke, Brown and Markelius want to see done, in addition to updating the data used to train the models, adding more people in a variety of demographics for training and feedback tasks.
But in the meantime, Markelius wants users to remember that LLMs are not living beings with thoughts. They have no intentions. “It’s just a glorified text prediction engine,” he said.
