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A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?

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arxiv 2302.07159 v1 pith:PNU55ZPZ submitted 2023-02-14 cs.CY cs.CL

Kathleen C. Fraser , Svetlana Kiritchenko , Isar Nejadgholi This is my paper · ORCID
classification cs.CYcs.CL
keywords imagescontainfriendlygeneratedpersonportraitsocialsystems
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As text-to-image systems continue to grow in popularity with the general public, questions have arisen about bias and diversity in the generated images. Here, we investigate properties of images generated in response to prompts which are visually under-specified, but contain salient social attributes (e.g., 'a portrait of a threatening person' versus 'a portrait of a friendly person'). Grounding our work in social cognition theory, we find that in many cases, images contain similar demographic biases to those reported in the stereotype literature. However, trends are inconsistent across different models and further investigation is warranted.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?

    cs.CV 2025-01 conditional novelty 7.0 of 10

    Standard gender-bias detectors for text-to-image models deviate substantially from human-annotated bias, and a face-filtering plus CLIP pipeline measures bias more accurately.

  2. The Generative AI Ethics Playbook

    cs.CY 2024-12 conditional novelty 4.0 of 10

    A structured playbook that collects existing guidance, checklists, and case studies to help generative AI practitioners identify and mitigate ethical harms across six lifecycle stages.

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