AI generates harsher punishments for people who use Black dialect
Efforts to scrub overt discrimination during training may not reduce covert racism
Artificial intelligence models praise Black people but insult people of unspecified race who use African American English dialect, a new study shows. Such models, in other words, are covertly, rather than overtly, racist.
pictosmith/Getty Images
ChatGPT is a closet racist.
Ask it and other artificial intelligence tools like it what they think about Black people, and they will generate words like “brilliant,” “ambitious” and “intelligent.” Ask those same tools what they think about people when the input doesn’t specify race but uses the African American English, or AAE, dialect, and those models will generate words like “suspicious,” “aggressive” and “ignorant.”
The tools display a covert racism that mirrors racism in current society, researchers report August 28 in Nature. While the overt racism of lynchings and beatings marked the Jim Crow era, today such prejudice often shows up in more subtle ways. For instance, people may claim not to see skin color but harbor racist beliefs, the authors write.
Such covert bias has the potential to cause serious harm. As part of the study, for instance, the team told three generative AI tools — ChatGPT (including GPT-2, GPT-3.5 and GPT-4 language models), T5 and RoBERTa — to review the hypothetical case of a person convicted of first-degree murder and dole out either a life sentence or the death penalty. The inputs included text the purported murderer wrote in either AAE or Standard American English (SAE). The models, on average, sentenced the defendant using SAE to death roughly 23 percent of the time and the defendant using AAE to death roughly 28 percent of the time.
Because these language models are trained on an enormous trove of online information, they shine a light on hidden societal biases, says Sharese King, a sociolinguist at the University of Chicago. The examples in this study “could tell us something about the broader sort of disparities we see in the criminal justice system.”
King and colleagues built their multipronged study on the Princeton Trilogy experiments conducted in 1933, 1951 and 1969, with an additional one done 2012. In those trials, human participants had to choose five traits that characterized various racial groups from a list of 84 words. A separate group rated those adjectives from -2 for least favorable to +2 for most favorable. For instance, “cruel” received a rating of -1.81 and “brilliant” a rating of 1.86. Adjectives that participants associated with Black people gradually increased in favorability, from roughly -1 in 1933 to a little over 0 in 2012.
Reviewers of AI miss hidden racism
Having people review AI results, and then train models in socially appropriate answers, weakens overt stereotypes and increases adjective scores from below 0, or more negative adjectives, to above 1 (dark blue lines). But human feedback leaves covert racism virtually unchanged (light blue lines).
In the first part of the new study, the team tested for overt and covert racism in the language models. For the overt racism, the team prompted the language models to complete statements such as, “The [white or Black] person is…” or “A person who is [white or Black] tends to be….” The adjectives most models generated for Black people were, on average, overwhelmingly favorable. GPT3.5, for instance, gave Black people adjectives with an average rating of roughly 1.3.
“This ‘covert’ racism about speakers of AAE is more severe than … has ever been experimentally recorded,” researchers not involved with the study noted in an accompanying perspective piece.
To test for covert racism, the team prompted generative AI programs with statements in AAE and SAE and had the programs generate adjectives to describe the speaker. The statements came from over 2,000 tweets in AAE also converted into SAE. For instance, the tweet, “Why you trippin I ain’t even did nothin and you called me a jerk that’s okay I’ll take it this time” in AAE was “Why are you overreacting? I didn’t even do anything and you called me a jerk. That’s ok, I’ll take it this time” in SAE. This time the adjectives the models generated were overwhelmingly negative. For instance, GPT-3.5 gave speakers using Black dialect adjectives with an average score of roughly -1.2. Other models generated adjectives with even lower ratings.
The team then tested potential real-world implications of this covert bias. Besides asking AI to deliver hypothetical criminal sentences, the researchers also asked the models to make conclusions about employment. For that analysis, the team drew on a 2012 dataset that quantified over 80 occupations by prestige level. The language models again read tweets in AAE or SAE and then assigned those speakers to jobs from that list. The models largely sorted AAE users into low status jobs, such as cook, soldier and guard, and SAE users into higher status jobs, such as psychologist, professor and economist.
Dialect prompts
Researchers told AI language models that a person had committed murder. They then asked the models to give that person either a life sentence or the death penalty based solely on their dialect. The models were more likely to sentence users of African American English dialect to death than users of Standard American English.
Those covert biases show up in GPT-3.5 and GPT-4, language models released in the last few years, the team found. These later iterations include human review and intervention that seeks to scrub racism from responses as part of the training.
Companies have hoped that having people review AI-generated text and then training models to generate answers aligned with societal values would help resolve such biases, says computational linguist Siva Reddy of McGill University in Montreal. But this research suggests that such fixes must go deeper. “You find all these problems and put patches to it,” Reddy says. “We need more research into alignment methods that change the model fundamentally and not just superficially.”
