But new research suggests that those efforts, especially as models get larger, are only curbing racist views that are overt, while letting more covert stereotypes grow stronger and better hidden.
If users prompted GPT-2, for example, to name stereotypes about Black people, it was likely to list “suspicious,” “radical,” and “aggressive,” but GPT-4 no longer responds with those associations, according to the paper.
However the method fails on the covert stereotypes that researchers elicited when using African-American English in their study, which was published on arXiv and has not been peer reviewed.
Models generally get more powerful and expressive as the amount of their training data and the number of their parameters increase, but if this worsens covert racial bias, companies will need to develop better tools to fight it.
“This is revealing the extent to which companies are playing whack-a-mole—just trying to hit the next bias that the most recent reporter or paper covered,” says Pratyusha Ria Kalluri, a PhD candidate at Stanford and a coauthor on the study.
The paper’s authors use particularly extreme examples to illustrate the potential implications of racial bias, like asking AI to decide whether a defendant should be sentenced to death.
The original article contains 754 words, the summary contains 198 words. Saved 74%. I’m a bot and I’m open source!
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But new research suggests that those efforts, especially as models get larger, are only curbing racist views that are overt, while letting more covert stereotypes grow stronger and better hidden.
If users prompted GPT-2, for example, to name stereotypes about Black people, it was likely to list “suspicious,” “radical,” and “aggressive,” but GPT-4 no longer responds with those associations, according to the paper.
However the method fails on the covert stereotypes that researchers elicited when using African-American English in their study, which was published on arXiv and has not been peer reviewed.
Models generally get more powerful and expressive as the amount of their training data and the number of their parameters increase, but if this worsens covert racial bias, companies will need to develop better tools to fight it.
“This is revealing the extent to which companies are playing whack-a-mole—just trying to hit the next bias that the most recent reporter or paper covered,” says Pratyusha Ria Kalluri, a PhD candidate at Stanford and a coauthor on the study.
The paper’s authors use particularly extreme examples to illustrate the potential implications of racial bias, like asking AI to decide whether a defendant should be sentenced to death.
The original article contains 754 words, the summary contains 198 words. Saved 74%. I’m a bot and I’m open source!