Social Biases through the Text-to-Image Generation Lens

• URL: http://arxiv.org/abs/2304.06034v1
• Date: Thu, 30 Mar 2023 05:29:13 GMT
• Title: Social Biases through the Text-to-Image Generation Lens
• Authors: Ranjita Naik, Besmira Nushi
• Abstract summary: Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software. We take a multi-dimensional approach to studying and quantifying common social biases as reflected in the generated images. We present findings for two popular T2I models: DALLE-v2 and Stable Diffusion.
• Score: 9.137275391251517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software by generating illustrative content with high photorealism starting from a given descriptive text as a prompt. Such models are however trained on massive amounts of web data, which surfaces the peril of potential harmful biases that may leak in the generation process itself. In this paper, we take a multi-dimensional approach to studying and quantifying common social biases as reflected in the generated images, by focusing on how occupations, personality traits, and everyday situations are depicted across representations of (perceived) gender, age, race, and geographical location. Through an extensive set of both automated and human evaluation experiments we present findings for two popular T2I models: DALLE-v2 and Stable Diffusion. Our results reveal that there exist severe occupational biases of neutral prompts majorly excluding groups of people from results for both models. Such biases can get mitigated by increasing the amount of specification in the prompt itself, although the prompting mitigation will not address discrepancies in image quality or other usages of the model or its representations in other scenarios. Further, we observe personality traits being associated with only a limited set of people at the intersection of race, gender, and age. Finally, an analysis of geographical location representations on everyday situations (e.g., park, food, weddings) shows that for most situations, images generated through default location-neutral prompts are closer and more similar to images generated for locations of United States and Germany.

Related papers

• Towards Geographic Inclusion in the Evaluation of Text-to-Image Models [25.780536950323683]
  We study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. We recommend steps for improved automatic and human evaluations.
  arXiv  Detail & Related papers  (2024-05-07T16:23:06Z)
• New Job, New Gender? Measuring the Social Bias in Image Generation Models [88.93677200602887]
  Image generation models can generate or edit images from a given text. Recent advancements in image generation technology, exemplified by DALL-E and Midjourney, have been groundbreaking. These advanced models are often trained on massive Internet datasets, making them susceptible to generating content that perpetuates social stereotypes and biases. We propose BiasPainter, a novel testing framework that can accurately, automatically and comprehensively trigger social bias in image generation models.
  arXiv  Detail & Related papers  (2024-01-01T14:06:55Z)
• Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models [72.06006736916821]
  We use synthetic images to probe two applications of text-to-image models, image editing and classification, for social bias. Using our methodology, we uncover meaningful and significant inter-sectional social biases in textitStable Diffusion, a state-of-the-art open-source text-to-image model. Our findings caution against the uninformed adoption of text-to-image foundation models for downstream tasks and services.
  arXiv  Detail & Related papers  (2023-12-05T14:36:49Z)
• TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models [22.076898042211305]
  We propose a general approach to study and quantify a broad spectrum of biases, for any TTI model and for any prompt. Our approach automatically identifies potential biases that might be relevant to the given prompt, and measures those biases. We show that our method is uniquely capable of explaining complex multi-dimensional biases through semantic concepts.
  arXiv  Detail & Related papers  (2023-12-03T02:31:37Z)
• Inspecting the Geographical Representativeness of Images from Text-to-Image Models [52.80961012689933]
  We measure the geographical representativeness of generated images using a crowdsourced study comprising 540 participants across 27 countries. For deliberately underspecified inputs without country names, the generated images most reflect the surroundings of the United States followed by India. The overall scores for many countries still remain low, highlighting the need for future models to be more geographically inclusive.
  arXiv  Detail & Related papers  (2023-05-18T16:08:11Z)
• Uncurated Image-Text Datasets: Shedding Light on Demographic Bias [21.421722941901123]
  Even small but manually annotated datasets, such as MSCOCO, are affected by societal bias. Our first contribution is to annotate part of the Google Conceptual Captions dataset, widely used for training vision-and-language models. Second contribution is to conduct a comprehensive analysis of the annotations, focusing on how different demographic groups are represented. Third contribution is to evaluate three prevailing vision-and-language tasks, showing that societal bias is a persistent problem in all of them.
  arXiv  Detail & Related papers  (2023-04-06T02:33:51Z)
• Stable Bias: Analyzing Societal Representations in Diffusion Models [72.27121528451528]
  We propose a new method for exploring the social biases in Text-to-Image (TTI) systems. Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts. We leverage this method to analyze images generated by 3 popular TTI systems and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents.
  arXiv  Detail & Related papers  (2023-03-20T19:32:49Z)
• How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions? [67.97752431429865]
  We study the effect on the diversity of the generated images when adding ethical intervention. Preliminary studies indicate that a large change in the model predictions is triggered by certain phrases such as 'irrespective of gender'
  arXiv  Detail & Related papers  (2022-10-27T07:32:39Z)
• DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models [73.12069620086311]
  We investigate the visual reasoning capabilities and social biases of text-to-image models. First, we measure three visual reasoning skills: object recognition, object counting, and spatial relation understanding. Second, we assess the gender and skin tone biases by measuring the gender/skin tone distribution of generated images.
  arXiv  Detail & Related papers  (2022-02-08T18:36:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.