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Sprint projectOct 25, 2026Chicago, Illinois, USA

Evidentiary Gaps in AI Collusion Detection: When Price Patterns Are Not Enough

Sharon T. Itonyo · Team Evidence-Aware Collusion Audit

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Evidentiary Gaps in AI Collusion Detection: When Price Patterns Are Not Enough

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We investigate whether an AI-collusion auditor can distinguish deliberately coordinated pricing from innocent parallel behavior when monitoring evidence is incomplete.

Using independently generated synthetic pricing scenarios, we compare price-only detection with an evidence-aware classifier across different levels of audit visibility. The evidence-aware approach detects 159 of 160 planted coordination cases under complete evidence, compared with 70 of 160 for the price-only baseline.

However, a separate benign stress test reveals a critical limitation: when two independent sellers follow the same public price recommendation, the evidence-aware detector flags all 160 cases under complete visibility.

The findings show that strong detection performance does not automatically establish the cause of observed coordination. Reliable accountability requires evidence about the origins and meaning of suspicious behavior, not just a convincing detection score.

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Cite this project

@misc{itonyo2026evidentiary,
  title = {{Evidentiary Gaps in AI Collusion Detection: When Price Patterns Are Not Enough}},
  author = {Sharon T. Itonyo},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/evidentiary-gaps-in-ai-collusion-detection-when-price-patterns-are-not-enough-8t27}},
  url = {https://apartresearch.com/sprints/projects/evidentiary-gaps-in-ai-collusion-detection-when-price-patterns-are-not-enough-8t27}
}

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