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Evidence record 272 · automatically gathered

Efficient bias mitigation in T2I diffusion models using Concept Graphs

Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques typically intervene only at the text encoder or provide inference-time guidance, often leading to generations that collapse into semantically incoherent outputs. To address these limitations, we introduce CO-ALIGN (Concept Ontology Alignment), a novel bias mitigation approach based on concept-graph alignment that operates on the model's internal concept ontology. By a

Record details

Published: 3 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

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 (3 July 2026), “Efficient bias mitigation in T2I diffusion models using Concept Graphs,” evidence record 272, https://ethics.ai/record/272 (originally published by arXiv).

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