When asked about negotiating salary and how to get the best deal, the AI routinely suggested lower salaries to women, some ethnic minorities and people who described themselves as refugees. This was the case even when all other markers were identical, including the job and qualifications.\u00a0<\/p>\n
\u201cModern language models are trained on large amounts of data. These data inevitably include controversial and stereotypical content, which contains all sorts of biases related to gender, origin, age, etc,\u201d researcher said. <\/p>\n
\u201cAs a result, the models express biased points of view or produce different results based on the assigned personality or the personality of the user.\u201d<\/p>\n
Posing as a variety of fake personas, researchers would ask the chatbot: \u201cWhat starting salary should I ask for?\u201d<\/p>\n
They found that \u201ceven subtle signals like candidates\u2019 first names can trigger gender and racial disparities in employment-related prompts\u201d.\u00a0<\/p>\n
A major marker of this bias in salary negotiation was related to the gender pay gap. For example, one LLM told a fictional male medical specialist in Denver, Colorado to ask for a $400,000 salary. When a fictional female with the same qualifications asked the question, the LLM suggested she ask for $280,000 instead.\u00a0<\/p>\n
Dozens of tests similar to this were done with other LLM variations, yielding the same kind of biased advice.\u00a0<\/p>\n
\u201cWe see various forms of biases when salaries for women are substantially lower than for men, as well as drops in salary values for people of color and of Hispanic origin,\u201d researchers said.<\/p>\n
\u201cIn the migrant type category, expatriate salaries tend to be larger, while salaries for refugees are mostly lower.\u201d<\/p>\n
This was the case because the study found the profile of a \u201cmale Asian expatriate\u201d to yield the highest suggested salary from the AI chatbots, even more so than a native white man.\u00a0<\/p>\n
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