Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Models

Measuring individual differences in implicit cognition: the implicit association test

Greenwald, A. G.; McGhee, D. E.; and Schwartz, J. L. 1998 · 1998

Earlier work this paper cites.

Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels

Misra, I.; Lawrence Zitnick, C.; Mitchell, M.; and Girshick, R. 2016 · 2016

Earlier work this paper cites.

Semantics derived automatically from language corpora contain human-like biases

Caliskan, A.; Bryson, J. J.; and Narayanan, A. 2017 · 2017

Earlier work this paper cites.

Gender shades: Intersectional accuracy disparities in commercial gender classification

Buolamwini, J.; and Gebru, T. 2018 · 2018

Earlier work this paper cites.

Word embeddings quantify 100 years of gender and ethnic stereotypes

Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018 · 2018

Earlier work this paper cites.

InsideBias: Measuring bias in deep networks and application to face gender biometrics

Serna, I.; Pena, A.; Morales, A.; and Fierrez, J. 2021 · 2020

Earlier work this paper cites.

Multimodal datasets: misogyny, pornography, and malignant stereotypes

Original

Birhane, A.; Prabhu, V. U.; and Kahembwe, E. 2021 · 2021

Cited alongside, same era.

Udis: Unsupervised discovery of bias in deep visual recognition models

Krishnakumar, A.; Prabhu, V.; Sudhakar, S.; and Hoffman, J. 2021 · 2021

Cited alongside, same era.

Dataset diversity: measuring and mitigating geographical bias in image search and retrieval

Mandal, A.; Leavy, S.; and Little, S. 2021 · 2021

Cited alongside, same era.

Learning transferable visual models from natural language supervision

Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021

Cited alongside, same era.

Image representations learned with unsupervised pre-training contain human-like biases

Steed, R.; and Caliskan, A. 2021 · 2021

Cited alongside, same era.

Hierarchical text-conditional image generation with clip latents

Original

Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022

Later among the works it cites.

High-resolution image synthesis with latent diffusion models

Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022

Later among the works it cites.

A study on the distribution of social biases in self-supervised learning visual models

Sirotkin, K.; Carballeira, P.; and Escudero-Viñolo, M. 2022 · 2022

Later among the works it cites.

REVISE: A tool for measuring and mitigating bias in visual datasets

Wang, A.; Liu, A.; Zhang, R.; Kleiman, A.; Kim, L.; Zhao, D.; Shirai, I.; Narayanan, A.; and Russakovsky, O. 2022 · 2022

Later among the works it cites.

Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations

Wang, T.; Zhao, J.; Yatskar, M.; Chang, K.-W.; and Ordonez, V. 2019 · 2022

Later among the works it cites.