Improving Resume Screening with NLP and Machine Learning: Addressing Efficiency and Fairness

The demand for logistical tools such as automated resume screening has grown in response to the need of organizations to streamline their hiring funnel. This approach of using manual review process is labour-intensive and eschews a potential for bias that can solned to both inefficiency and unfairness in their hiring decisions. The focus of this paper lies in using natural language processing (NLP) along with Machine Learning(ML) to improve the pace and fairness of different resume screening systems. We present a scalable and automated solution to make the hiring process more fair by reducing bias in resumes using NLP techniques or extracting useful information from each resume and develop ml models to analyse profile of candidates. The research addresses critical problems which are to process the unformalized data coming in from resumes, to deal with algorithmic biases and also model performance for a broad range of jobs. We also delve into the questions of data and ethics in how AI can be used for recruitment, as well discuss approaches to help are bias from surfacing. These numbers speak for themselves, it demonstrates that by using NLP with ML on both ends makes recruiting a breeze and in addition driving increased fairness at the same time finally intended again: job-related credentials can be de-emerged (candidate focus) without losing proper measures of quality. Those findings provided an aerial view of what should come next for ensuring AI-powered talent and recruitment systems operate more blandly. Submit a job and in the contemporary job market is generating hundreds of applications that must be reviewed fairly.