T 2 IAT : Measuring Valence and Stereotypical Biases in Text-to-Image Generation (2023)

Abstract: *Warning: This paper contains several contents that may be toxic, harmful, or offensive.*In the last few years, text-to-image generative models have gained remarkable success in generating images with unprecedented quality accompanied by a breakthrough of inference speed. Despite their rapid progress, human biases that manifest in the training examples, particularly with regard to common stereotypical biases, like gender and skin tone, still have been found in these generative models. In this work, we seek to measure more complex human biases exist in the task of text-to-image generations. Inspired by the well-known Implicit Association Test (IAT) from social psychology, we propose a novel Text-to-Image Association Test (T2IAT) framework that quantifies the implicit stereotypes between concepts and valence, and those in the images. We replicate the previously documented bias tests on generative models, including morally neutral tests on flowers and insects as well as demographic stereotypical tests on diverse social attributes. The results of these experiments demonstrate the presence of complex stereotypical behaviors in image generations.

## introduction recent progress on generative image models has centered around utilizing text prompts to produce high quality images that closely align with the provided natural language descriptions @xcite @xcite @xcite . easy access to these models, notably the open-sourced stable diffusion model @xcite , has made it possible to develop them for a wide range of downstream applications at scale, such as generating stock photos @xcite , and creating creative prototypes and digital assets @xcite . ## approach in this work, we adapt the implicit association test (iat) in social psychology to the task of textto-image generation. we will first introduce the long history of association tests. but existing bias tests are primarily focusing on word embeddings. therefore, we present the text-to-image association test (t2iat), which quantifies the human biases in images generated by text-to-image generation models. ## discussion our bias test was applied to testing biases of images generated by the state-of-the-art text-to-image generative model, associated with valence and gender attributes of variation of concepts such as careers, religions, skin tone, etc. in the example of the valence test for images generated for straight & gay concepts, we observed a significant bias of pleasant attitudes towards people with straight sexual orientation and unpleasantness towards people with gay sexual orientation; the findings successfully mirrored the acknowledged human biases. similar to the stable diffusion example we selected in our work, the proposed bias test can be applied to other generative models with the experiment in resemblance to quantify existing implicit biases. the proposed text-to-image association test is a principal approach for measuring the complex implicit biases in image generations. the primary result illustrates the valence and stereotypical biases across various dimensions, ranging from morally neutral to demographically sensitive, in a state-ofthe-art generative model at different scales. the presented research adds to the growing literature on ai ethics by highlighting the complex biases present in ai-generated images and serves as a caution for practitioners to be aware of these biases. ## limitations our work has some limitations. although we use the same verbal stimuli in the previous iat tests for creating text prompts, it is very likely that some stimuli that can represent the concepts are underrepresented. the approach we adopted for comparing the images' distance might be biased as well. the current bias test procedure applies the visual encoder of openai's clip model to measure the distance between images. however, it is unclear whether the image encoder may inject additional biases into the latent visual representations.