Measuring Social Biases in Grounded Vision and Language Embeddings
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by
Candace Ross, Boris Katz, Andrei Barbu
2020
Abstract
We generalize the notion of social biases from language embeddings to
grounded vision and language embeddings. Biases are present in grounded
embeddings, and indeed seem to be equally or more significant than for
ungrounded embeddings. This is despite the fact that vision and language can
suffer from different biases, which one might hope could attenuate the biases
in both. Multiple ways exist to generalize metrics measuring bias in word
embeddings to this new setting. We introduce the space of generalizations
(Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations
answer different yet important questions about how biases, language, and vision
interact. These metrics are used on a new dataset, the first for grounded bias,
created by augmenting extending standard linguistic bias benchmarks with 10,228
images from COCO, Conceptual Captions, and Google Images. Dataset construction
is challenging because vision datasets are themselves very biased. The presence
of these biases in systems will begin to have real-world consequences as they
are deployed, making carefully measuring bias and then mitigating it critical
to building a fair society.
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