Quantifying Bias in Text-to-Image Generative Models

Bias in text-to-image (T2I) generation can propagate unfair social representations and may be exploited to push ulterior agendas. These biases raise concerns on the dependability and fairness of models that have become widely popular and readily available for public consumption. Existing works in T2I bias analysis typically focus on social biases. We look beyond that and instead propose an evaluation methodology to quantify general bias in T2I generative models without any preconceived notion. We introduce a suite of three metrics; namely, distribution bias, Jaccard hallucination and generative miss-rate, to extensively appraise general model bias. To validate the efficacy of these metrics, we also introduce a backdoor-inspired strategy, which provides a convenient handle over the extent of bias in a model for controlled analysis. We assess T2I models implementing six widely used pipelines in this domain. Our extensive analysis covers both general and task-oriented scenarios, employing over 105 K generated images. For prior art comparison, it also encompasses social bias analysis. Moreover, we also extend our technique to analyze bias in seven popular captioned image datasets. Our experiments establish that our approach is objective, domain-agnostic and it consistently measures different forms of T2I model biases. To further research efforts into T2I model biases, we have developed an open-source web application and practical implementation of this work, which is available on HuggingFace. All relevant code is also publicly available on GitHub.