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Evidence record 722 · automatically gathered

The Unseen Hand: Manipulating Model Fairness and SHAP with Targeted Identity Re-Association Attacks

As machine learning models grow more influential and opaque, algorithmic fairness and explainability are critical for ensuring accountability. However, we demonstrate that these auditing mechanisms are themselves vulnerable to subtle manipulation, camouflaging the influence of protected features. While prior work on data-agnostic attacks has exposed this vulnerability, they leave behind detectable artifacts that compromise their stealth. We introduce Targeted Identity Re-Association (TIRA) attac

Record details

Published: 22 June 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.

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How to cite this record

ethics.ai (22 June 2026), “The Unseen Hand: Manipulating Model Fairness and SHAP with Targeted Identity Re-Association Attacks,” evidence record 722, https://ethics.ai/record/722 (originally published by arXiv).

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