New Filipino benchmark pairs show multilingual models choose sexist and anti-queer sentences measurably more often than neutral alternatives, with models trained on more Filipino text showing the strongest bias.
In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , AIES ’21, page 254–265, New York, NY , USA
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Filipino Benchmarks for Measuring Sexist and Homophobic Bias in Multilingual Language Models from Southeast Asia
New Filipino benchmark pairs show multilingual models choose sexist and anti-queer sentences measurably more often than neutral alternatives, with models trained on more Filipino text showing the strongest bias.