Fake job postings have become prevalent in the online job market, posing significant challenges to job seekers and employers. Despite the growing need to address this problem, there is limited research that leverages deep learning techniques for the detection of fraudulent job advertisements. This study aims to fill the gap by employing a Bidirectional Long Short-Term Memory (Bi-LSTM) model to identify fake job advertisements. Our approach considers both numeric and text features, effectively capturing the underlying patterns and relationships within the data. The proposed model demonstrates a superior performance, achieving a 0.91 ROC AUC score and a 98.71% accuracy rate, indicating its potential for practical applications in the online job market. The findings of this research contribute to the development of robust, automated tools that can help combat the proliferation of fake job postings and improve the overall integrity of the job search process. Moreover, we discuss challenges, future research directions, and ethical considerations related to our approach, aiming to inspire further exploration and development of practical solutions to combat online job fraud.

Technology has advanced manifold in the last decade. The use of technology in almost all domains has seemingly made us increasingly dependent on it. Consequently, Artificial Intelligence (AI) and Machine Learning (ML) have significantly increased in popularity and are being used in numerous fields. These technologies have simplified our work, allowing us to simultaneously perform many tasks. Regardless of these technological advancements, there has been a significant increase in fraudulent activities. AI and ML are used in the modern recruiting process to easily filter data and recommend the most qualified candidates for the job.

Imagine you try to determine whether a job posting is fake or not. How do you test which of these features are the most accurate in predicting fraud? More importantly, since different methods might guess different labels, how do you pick a label based on these guesses? That is when Snorkel comes in handy. Snorkel is an open-source Python library for programmatically building training datasets without manual labeling.

After more than a year of the covid-19 pandemic, millions of people are searching for employment in the United States. AI-powered interview software claims to help employers sift through applications to find the best people for the job. Companies specializing in this technology reported a surge in business during the pandemic. But as the demand for these technologies increases, so do questions about their accuracy and reliability. In the latest episode of MIT Technology Review's podcast "In Machines We Trust," we tested software from two firms specializing in AI job interviews, MyInterview and Curious Thing. And we found variations in the predictions and job-matching scores that raise concerns about what exactly these algorithms are evaluating.