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Evidence record 4307 · automatically gathered

Beyond Performance Disparities: A Three-Level Audit of Representational Harm in CelebA

Large-scale facial datasets like CelebA are widely used in computer vision, yet the cultural biases embedded in their labels remain underexplored. Fairness research has distinguished representational from allocational harms, but audits of computer vision datasets have mostly examined categorical labels, leaving open how such harms appear in learned features and model attention. This paper examines CelebA at three levels: dataset structure, learned feature weights, and spatial attention, focusing

Record details

Published: 14 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Transparency
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.

Shared topic: Bias & fairness · Shared topic: Transparency

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Shared topic: Bias & fairness · Shared topic: Transparency

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Shared topic: Bias & fairness · Shared topic: Transparency

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Shared topic: Bias & fairness · Shared topic: Transparency

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Shared topic: Bias & fairness · Shared topic: Transparency

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How to cite this record

ethics.ai (14 May 2026), “Beyond Performance Disparities: A Three-Level Audit of Representational Harm in CelebA,” evidence record 4307, https://ethics.ai/record/4307 (originally published by arXiv).

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