Quantifying Societal Bias Amplification in Image Captioning

By Yusuke Hirota, Yuta Nakashima, Noa García. 2022

63 citations recorded by the source corpus.

We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and evaluate the societal bias in captions are not yet standardized. We provide a comprehensive study on the strengths and limitations of each metric, and propose LIC, a metric to study captioning bias amplification. We argue that, for image captioning, it is not enough to focus on the correct prediction of the protected attribute, and the whole context should be taken into account. We conduct extensive evaluation on traditional and state-of-the-art image captioning models, and surprisingly find that, by only focusing on the protected attribute prediction, bias mitigation models are unexpectedly amplifying bias.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling (2023, background): “This is particularly concerning as one of the best established phenomena in the study of bias in deep learning models is bias amplification—the fact that social biases in deep learning models tend to be more extreme than those found in their training data (Zhao et al., 2017; Hirota et al., 2022; Hal”
DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models (2022, unknown): “[6, 5, 58, 9, 73, 28, 62, 27] examine social biases in image-text datasets.”