Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection

Abstract: The advancement of LLMs has made it difficult to differentiate human-written text from AI-generated text. Several AI-text detectors have been developed in response, which typically utilize a fixed global threshold (e.g., $\theta = 0.5$) to classify machine-generated text. However, one universal threshold could fail to account for distributional variations by subgroups. For example, when using a fixed threshold, detectors make more false positive errors on shorter human-written text, and more positive classifications of neurotic writing styles among long texts. These discrepancies can lead to misclassifications that disproportionately affect certain groups. We address this critical limitation by introducing FairOPT, an algorithm for group-specific threshold optimization for probabilistic AI-text detectors. We partitioned data into subgroups based on attributes (e.g., text length and writing style) and implemented FairOPT to learn decision thresholds for each group to reduce discrepancy. In experiments with nine AI text classifiers on three datasets, FairOPT decreases overall balanced error rate (BER) discrepancy by 12\% while minimally sacrificing accuracy by 0.003\%. Our framework paves the way for more robust classification in AI-generated content detection via post-processing.