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Evidence record 4409 · automatically gathered

Context Matters: Auditing Gender Bias in T2I Generation through Risk-Tiered Use-Case Profiles

Text-to-image (T2I) generative models are increasingly used to produce content for education, media, and public-facing communication, and are starting to be integrated into higher-impact pipelines. Since generated images tend to reinforce stereotypes, producing representational erasure via "default" depictions and shaping perceptions of who belongs in certain roles, a growing body of work has proposed metrics to quantify gender bias in T2I outputs. Yet existing evaluations remain fragmented. Met

Record details

Published: 13 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Children & education · Transparency
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (13 May 2026), “Context Matters: Auditing Gender Bias in T2I Generation through Risk-Tiered Use-Case Profiles,” evidence record 4409, https://ethics.ai/record/4409 (originally published by arXiv).

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