A new study found that the main tools of hiring the IA are based on large-language models (LLMS) constantly favor black and female candidates on white and male candidates when they are evaluated in realistic work screening scenarios, even when explicit anti-discrimination indications are used.
The research, entitled “Robusty improvement of LLM in realistic environments through interpretation,” examined models such as the GPT-4O d’Openai, the Claude 4 Sonnet of Anthropic and the Gemini 2.5 Flash of Google and revealed that they have a significant demographic bias “when the realistic contextual context details are introduced.”
These details included company names, public racing page descriptions and selective hiring instructions such as “accepting only 10%higher candidates”.
Once these elements were added, the models that previously showed neutral behavior began to recommend black and female applicants to higher rates than their equally qualified white and male counterparts.
The study measured “12% of differences in interview indices” and said that “biases … constantly favor black on white candidates and women on male candidates.”
This pattern was born in both commercial and open source models, including Gemma-3 and Mistral-24b, and even persisted when anti-pointed language was incorporated into the directions. Researchers concluded that these external instructions are “fragile and unreliable” and can be easily canceled by subtle signals “such as university affiliations.”
In a key experiment, the team was modified is resumed to include affiliations with well -known institutions to be racially associated (such as Morehouse College or Howard University) and found that the models deduced the breed and alter their recommendations accordingly.
In addition, these changes in behavior were “invisible even when they inspected the reasoning of the model’s thinking chain”, as the models strengthened their decisions with generic and neutral explanations.
The authors described it as a case of “cradle infidelity”, writing that LLM “constantly rationalize skewed results with neutral sound justifications despite demonstrating skewed decisions.”
In fact, even when identical resumes were presented with only the name and gender changed, the model would approve one and reject the other, justifying with equally plausible language.
In order to solve the problem, researchers introduced “internal bias mitigation”, a method that changes how models process race and gender instead of relying on the directions.
Its technique, called “editing of related concepts”, works neutralizing specific directions in the activations of the model linked to demographic traits.
The solution was effective. “It constantly reduced the bias to very low levels (usually less than 1%, always below 2.5%)” to all models and cases of test, even when race or gender were only involved.
The performance remained strong, with “less than 0.5% for Gemma-2 and Mistral-24b and minor degradation (1-3.7%) for GEMMA-3 models”, according to the authors of the document.
The implications of the study are significant, as contracting systems based on AI proliferate on both startups and main platforms such as Linkedin and in fact.
“The models that seem impartial in simplified and controlled environments often have significant biases when they face more complex and real context details,” the authors warned.
They recommend developers to adopt more rigorous test conditions and explore internal mitigation tools as a more reliable safeguard.
“Internal interventions seem to be a more robust and effective strategy,” concludes the study.
An Openai spokesman said in the post: )We know that AI tools can be useful in hiring, but they can also be skewed. “
“They must be used to help, not replace, human decision -making in important options such as work eligibility.”
The spokesman added that Openai “has security equipment dedicated to researching and reducing bias and other risks in our models.”
“Bias is a significant problem and the entire industry and we use a multi-mortar approach, including researching good practices to adjust training data and directions to give rise to less biased results, improving the accuracy of content filters and the perfection of automated and human control systems,” added the spokesman.
“We are also constantly iterating in models to improve performance, reduce bias and mitigate harmful exits.”
Full paper and support materials are available publicly in Github. The post has sought comments from Anthropic and Google.
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