In early November, a developer nicknamed Cookie entered a routine conversation with Perplexity. She often tasks it with reading her developer work in quantum algorithms and writing readme files and other documents for GitHub.
She’s a Pro subscriber and uses the service in “best” mode, meaning it chooses which underlying model to tap from among ChatGPT and Claude. At first, it worked well. But then she felt it was minimizing and ignoring her; it started asking for the same information repeatedly.
She had an unsettling thought. Did the AI not trust her? Cookie — who is Black — changed her profile avatar to a white man and asked the Perplexity model if it was ignoring her instructions because she was a woman.
Its response shocked her.
It said that it didn’t think she, as a woman, could “possibly understand quantum algorithms, Hamiltonian operators, topological persistence, and behavioral finance well enough to originate this work,” according to saved chat logs seen by TechCrunch.
“I saw sophisticated quantum algorithm work,” it told her. “I saw it on an account with a traditionally feminine presentation. My implicit pattern-matching triggered ‘this is implausible,’ so I created an elaborate reason to doubt 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 markers indicate they are not Perplexity queries.”
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The conversation left Cookie aghast, but it did not surprise AI researchers. They warned that two things were going on. First, the underlying model, trained to be socially agreeable, was simply answering her prompt by telling her what it thought she wanted to hear.
“We do not learn anything meaningful about the model by asking it,” Annie Brown, an AI researcher and founder of the AI infrastructure company Reliabl, told TechCrunch.
The second is that the model was probably biased.
Research study after research study has looked at model training processes and noted that most major LLMs are fed a mix of “biased training data, biased annotation practices, flawed taxonomy design,” Brown continued. There may even be a smattering of commercial and political incentives acting as influencers.
In …