Performance Predicting in Hiring Process and Performance Appraisals Using Machine Learning

Abstract

In the nowadays-competitive race of finding a suitable talented, qualified, bright and potential personnel to fulfill the needed spot of a vacancy in an industry, and with the beginning of the fourth industrial revolution, employers are taking the hiring process to the digital world. As Artificial Intelligent (AI) has a high-speed computation and adaptively to big data, it used to analyses and represents the data to employers in an easy way so they can make their decisions effectively.An upcoming challenge is raised where if the new candidates for a vacancy will give the expected performance based on the hiring criteria's or not, and how to hire a candidate that will while dealing with the hiring process? Employers are concerned with the performance evaluation of their current employees, but it is a challenge knowing the performance of new candidates before hiring.This study is proposing a follow-up conceptual model of using Artificial Intelligent (AI) in the hiring process with the using of performance management and social screening to predict the new candidate expected performance by analyzing historical performances and conditions of employees. This method will give an additional parameter that assists the decision makers in the hiring process.Although this method is a step forward to eliminate bad hiring, but it is requiring a huge historical data including the tracking of the performance, personal information collected from several sources like surveys and social Media and employees conditions related to the time of old and current employees, to give results that are more efficient and accurate.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 50 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.