A detection score is the beginning of analysis, not the conclusion. This capstone module teaches you to build confidence in your findings and communicate them effectively to stakeholders.
Beyond the Score
Detection tools output numbers. Your job is to turn those numbers into defensible conclusions. This requires understanding base rates, calibrating confidence, accounting for context, and presenting findings with appropriate uncertainty.
High Confidence
Multiple tools agree, strong indicators
Moderate
Likely AI, requires human review
Inconclusive
Mixed signals, cannot determine
Likely Human
Low AI probability, standard review
The Base Rate Problem
If only 5% of submissions are AI-generated and your detector has a 10% false positive rate, most flagged content is actually human-written. Understanding Bayesian reasoning is essential for interpreting detection output in real-world contexts where AI-generated content is a minority of submissions.
Building a Decision Framework
Professional analysts use structured frameworks rather than gut feelings. A good framework specifies the decision threshold, required evidence standard, escalation criteria, and documentation requirements before analysis begins.
Evidence Standards
Level 1 — Screening: Single tool score above threshold. Used for flagging only, not action.
Level 2 — Investigation: Multiple tools agree. Qualitative markers present. Sufficient for further inquiry.
Level 3 — Determination: Ensemble analysis, contextual review, and pattern analysis all converge. Sufficient for formal action.
Communicating Findings
Stakeholders — academic administrators, editors, legal teams — need clear, defensible reports. Never present a detection score as proof. Frame findings as probability assessments with documented methodology, known limitations, and recommended next steps.
This capstone module draws on everything covered in the AI Detection Fundamentals course. For hands-on practice with the tools discussed, visit our Tools & Resources page.