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Evidence record 3074 · automatically gathered

WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation

The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups. A key obstacle to studying and mitigating such biases is the lack of face detection datasets with sensitive feature annotations. To address this gap, we introduce WIDER-FAIR, a new dataset built on the widely used WIDER-FACE benchmark, manually annotated with the perceived ethnicity and sex of each face. The datase

Record details

Published: 30 June 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness
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 (30 June 2026), “WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation,” evidence record 3074, https://ethics.ai/record/3074 (originally published by arXiv fairness query).

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