The Application of Artificial Intelligence in Resume Screening

Abstract

Abstract : Feeding new pool resources into any enterprise is critical for its growth strategy. More important, is how it suits current personnel requisites and organizational needs or objectives- which makes recruiting such a vital part of any business expansion plan. Unfortunately, without adequate automation initiatives or software input systems - traditional hiring efforts inherently rely on many people-intensive tasks drawing from probability-based outcomes making them prone to distortions from conscious biases or even human errors. This barrier underscores the value that Artificial Intelligence (AI) technologies present in automating most human-intensive activities during candidate searches/acquisition moments effectively generating far-reaching gains and saving time while attuning operations towards unaffected efficacy-producing outcomes. In this study, therefore, our objective seeks to explore how we can use AI-derived technologies such as machine learning (ML) & deep learning (DL) algorithms to support efficient biasfree, objective, and effective recruitment in the hiring process. Towards that goal, we first provide context through an overview of the recruitment process and its attendant challenges. The following comprehensive literature review dwells extensively on extant research already conducted mostly around leveraging AI technologies in recruitment activities. Connecting complementary sub-fields like Natural Language Processing(NLP), and Computer Vision(CV)and providing a comparative analysis of various state-of-the-art resume analysis methodologies and how best to deploy them for optimum results. Ultimately this thesis demonstrates the potential impact of both ML&DL algorithms deployment in transforming staffing processes from manual tasks to streamlined objectivity improving overall efficiency while maintaining a stronger sense of quality about candidate suitability-for-role determinations- offering valuable insights that prove beneficial for future research as well as development initiatives.