Abstract

Online portals have become commonplace for internships and job opportunities. With the increasing number of fraudulent internship and job offers, students find it difficult to differentiate between legitimate opportunities. Students face various risks such as fake recruiters, phishing, registration fee frauds, unrealistic offers, and other deceptions. This paper presents a study on an explainable artificial intelligence approach to detect fraudulent internship and job offers. It reviews existing artificial intelligence, machine learning, deep learning, natural language processing, and transformer based recruitment fraud detection algorithms and techniques and explainability methods such as SHAP and LIME for providing an interpretable explanation for a model’s outcome. It highlights the limitations of previous approaches such as using generic datasets and lacking internship-specific features, limited explainability and no user-centric explanations.

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