Sources and supporting resources

NIST Gen AI Profile (2024)

Gen AI can produce confident falsehoods; evaluate performance in the intended setting, verify sources, and document oversight.

Original address for later reading: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

Turnitin detection FAQ (reviewed September 24, 2026)

The indicator estimates the share of qualifying prose flagged by the model. It is not a probability of misconduct or a determination of misconduct; Turnitin advises against using it alone for adverse action.

Original address for later reading: https://guides.turnitin.com/hc/en-us/articles/28477544839821-Turnitin-s-AI-writing-detection-capabilities-FAQs

Liang et al., Patterns (2023)

This study found false-positive disparities for the non-native English writing and detectors it tested. It does not establish error rates for all current tools.

Original address for later reading: https://arxiv.org/abs/2304.02819

Al Ali, Helcl & Libovicky, EACL SRW (2026)

A later study centered on Czech writing found no systematic non-native-speaker bias across its tested detector families. Context, language, and detector matter.

Original address for later reading: https://arxiv.org/abs/2602.05769

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