Detecting Deceptive Recruitment: A Signal-theoretic Machine Learning Framework for Early Identification of Labour Exploitation
Researchers build a multimodal ML framework detecting deceptive job advertisements used for labour exploitation, achieving ROC-AUC 0.87–0.97 across individual modalities.
Using 464 verified cases (164 deceptive, 300 legitimate) gathered through anti-slavery charities across nine origin countries and 21 industries, researchers combined computer vision, NLP, and semantic embeddings to flag exploitative recruitment ads. SHAP analysis identified text quality, readability indices, risk keyword density, and visa sponsorship mentions as top discriminators, with combined modalities reaching ROC-AUC up to 0.97. Findings are operationalized in a proof-of-concept decision support system providing interpretable risk scores for practitioners.
- 464 verified deceptive and legitimate job ads from nine origin countries and 21 industries
- Multimodal CV/NLP/embedding models achieve ROC-AUC 0.87–0.97
- SHAP shows text quality and risk-language keywords are primary discriminators
- Proof-of-concept tool provides interpretable risk scores for anti-slavery practitioners
From the original article
1 min read at arxiv.orgDeceptive online job advertisements have emerged as a primary pathway into forced labour, yet systematic detection methods remain underdeveloped due to data scarcity and absence of empirically validated indicators.
By Sajid Siraj · arxiv.org
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