Corpus and Baselines for Distinguishing Authentic, AI-Generated, and AI-Enhanced Resumes

Abstract

Job applicants are increasingly turning to generative AI to create or enhance their resumes, leading to challenges in fairness, integrity, and efficiency of modern recruitment processes. We present the first curated corpus of resumes annotated as to whether they are authentic, AI-enhanced, or fully AI-generated. The corpus is balanced across the three classes, comprising 420 resumes spanning five job descriptions in the Information Technology (IT) sector, with the authentic resumes anonymized. We establish strong baselines for this task using traditional and neural supervised machine learning approaches, including Logistic Regression, SVM, Random Forest, XGBoost, BERT, and Longformer. For the featurized approaches, we pair sparse TF-IDF (word/character n-grams) with style features capturing length, punctuation, casing, contractions, lexical diversity (type-token ratio [TTR], number of hapax legomena), n-gram uniqueness, readability indices, and sentiment. Our analysis reveals systematic differences between the classes: AI-generated text features shorter, more uniform sentences, and fewer contractions; AI-enhanced text has the highest uniqueness and TTR; and authentic text has the widest variance across all features. XGBoost is the best performing method, achieving 95.29% accuracy and an F1 of 0.953. We make the corpus available for other researchers to build upon our work. We also benchmark two leading off-the-shelf AI–text detectors on our 420-resume corpus. Despite strong reports in other domains, Originality attains only 55.7% accuracy overall (71/140 authentic, 81/140 AI-generated, 82/140 AI-enhanced correct), and Writer attains 25.0%, with the largest failures on AI-enhanced resumes, highlighting domain shift and cautioning against uncalibrated deployment.

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