The Adverse Effects of Artificial Intelligence on Employment Process: A Critical Analysis

Abstract

The advent of artificial intelligence (AI) and its integration into the workplace and employment process is driven by the growing need for efficiency in human resource management practices. Before AI’s incorporation, the employment process of recruitment, selection and placement was predominantly human centric, labour intensive and time consuming. This traditional approach however allowed for a nuanced of applicants, factoring subtle cues and personal interaction which are overlooked by AI automated systems. Despite the efficiency and transformation that AI technologies bring to the workplace, its integration into the employment process has not been without significant implications. Application of AI in employment is raising concerns among scholars and AI experts about displacement of workers in wider set of jobs and tasks across most of the skills and wage spectrum. The predominant focus of current researches and studies on impact of AI in employment are on investigating its economic and efficiency outcomes while its adverse effects on employee morale, workplace dynamics and other social- psychological impacts were largely under-researched. A critical analysis of this gap within the context of adverse effect of AI integration to the employment process is the primary focus of the paper. Being an enquiry after the fact, the ex-post-facto research design was adopted while the Socio-Technical System Theory was used to anchor the theoretical analytical framework of the paper. The paper found that the level of AI integration into the workplace and employment process in the US and Europe has reached 25 percent with significant adverse effects such as major job displacements with a projection by the McKinsey Global Institute 2018 study that by 2030 when AI integration into the workplace attains 80 percent more than 50 percent of global industrial activities would be driven by AI systems resulting in the loss of over 800 million global jobs in the manual and mid-skilled categories to automation. The paper also found evidences of AI algorithm bias against the employment of certain categories of employees as well as breaches of employee’s personal privacy by AI machines. Some policy recommendations were suggested as a way forward.