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RAID Study Sets New Benchmark for AI Detector Evaluation


RAID is a collaborative effort by leading academic institutions to establish a rigorous and standardized assessment framework for AI text detection tools.

COLLINGWOOD , ONTARIO, CANADA, May 21, 2024 / -- In an unprecedented collaborative effort, researchers from UPenn, University College London, King’s College London, and Carnegie Mellon University have released the most comprehensive study to date on AI detector efficacy, titled “RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors.” This groundbreaking research establishes a new standard for evaluating the accuracy and robustness of AI detectors.

Study Details:


- The study rigorously evaluated 12 leading AI detectors.
- It tested against 11 advanced text generation models, including ChatGPT and GPT-4.
- The research spanned 8 diverse domains of text.
- It challenged detectors with 11 types of sophisticated adversarial attacks.
- The dataset encompassed an extensive 6,287,820 text records.

Dataset: The complete dataset is publicly available on GitHub.

Evaluation Standard: The study applied a 5% false positive threshold across all tests to ensure a balanced measure of precision and recall, critical for real-world applicability.

Key Findings:

Originality.AI Recognized as the Most Accurate AI Detector:

Most Accurate on Base Dataset: Originality.AI achieved an outstanding 85% accuracy, outperforming the nearest competitor, which scored 80%.

Top Performer on Adversarial Datasets: Originality.AI excelled, ranking first in 9 out of 11 adversarial tests, demonstrating unparalleled resilience against techniques designed to evade detection.

Leading Across All Domains: Originality.AI led the field in 5 out of 8 content domains and secured second place in the remaining 3, showcasing its versatility and reliability across various types of text.

Exceptional Paraphrase Detection: Originality.AI's detection capabilities were particularly notable in identifying paraphrased content, achieving a remarkable 96.7% accuracy compared to an average of 59% among other detectors.

The RAID study represents a significant milestone in the field of AI detection. It provides a robust framework for evaluating the efficacy of AI detectors, ensuring that they meet the highest standards of accuracy and reliability.

For more detailed findings and methodologies, the full study is available at

Jonathan Gillham
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