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Evidence record 17762 · automatically gathered

Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests

arXiv:2608.06908v1 Announce Type: cross Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT). WEAT is a bias measurement method widely used in both computational social science and AI fairness research. It relies on cosine similarity as a measure of semantic association, which assumes that the embedding space is approximately isotropic. However, prior work has reported that many widely used language m

Record details

Published: 10 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness
Retrieved: 10 August 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.

Same publisher byline · Shared topic: Bias & fairness

Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests

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Shared topic: Bias & fairness

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arXiv · 10 August 2026

Shared topic: Bias & fairness

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How to cite this record

ethics.ai (10 August 2026), “Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests,” evidence record 17762, https://ethics.ai/record/17762 (originally published by arXiv cs.CY).

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