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Stratagems #16: Mark Left a Hole in His AI Audit. Lena Counted Every Layer.
TL;DR: A covert audit uncovers an AI evaluation dataset being systematically trimmed by an auto-labeling pipeline, revealing a deliberate bias in performance metrics. The discovery points to a subtle, repeating manipulation underlying multiple reports over time.
Mark discovers that the FairPay evaluation dataset hides low-scoring samples by automatic labeling thresholds. Over three months, the same exclusion pattern repeats across snapshots, culminating in a non-human config file that reveals Pulse AI's training pipeline auto-output behavior. This implies selective reporting and potential integrity issues in the evaluation workflow. The story merges strategic deception with a technical audit finding, highlighting how hidden pipelines can mislead stakeholders.
Question for the room: Have you encountered auto-labeling or evaluation-pipeline biases in your audits, and how did you uncover or validate them?
— via dev.to
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