Measuring Social Biases in Grounded Vision and Language Embeddings

Candace Ross, Boris Katz, Andrei Barbu


Abstract
We generalize the notion of measuring social biases in word embeddings to visually grounded word 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 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.
Anthology ID:
2021.naacl-main.78
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
998–1008
Language:
URL:
https://aclanthology.org/2021.naacl-main.78
DOI:
10.18653/v1/2021.naacl-main.78
Bibkey:
Cite (ACL):
Candace Ross, Boris Katz, and Andrei Barbu. 2021. Measuring Social Biases in Grounded Vision and Language Embeddings. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 998–1008, Online. Association for Computational Linguistics.
Cite (Informal):
Measuring Social Biases in Grounded Vision and Language Embeddings (Ross et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.78.pdf
Video:
 https://aclanthology.org/2021.naacl-main.78.mp4
Code
 candacelax/bias-in-vision-and-language
Data
Conceptual Captions