Show Notes
Should you strip suspected AI-generated text out of a training set? The position here is no, not blind. Slop detectors are classifiers that flag one to five percent of clean human writing as fake, and they misfire on dense expert prose and standardized legal boilerplate, so teams in financial services and legal have deleted the backbone of their corpus and watched accuracy fall. A recent experiment found filtering degraded performance more than the contamination removed.
You leave with a Monday plan: quarantine and hand-sample flagged items rather than deleting, tag provenance at ingest instead of at training time, and read the unknown-provenance estimates from dbt Labs and MIT Sloan Management Review with the vendor incentive in mind.
0:00 Why a filtered dataset made accuracy drop 0:50 How often detectors flag human writing 1:36 Contract summarization corpus gutted by filtering 2:26 Data provenance as a governance problem 3:28 Quarantine flagged data instead of deleting 4:00 Test the detector on known-good data
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