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Accepted Paper:

AI vs. employment: is it an opportunity or a challenge in human resources management?  
lei SHI (National Academy of Innovation Strategy, CAST) liuhua yang

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Short abstract:

Generative AI saves a lot of time in dealing with many routine jobs in human resources management. However, like a sword with two edges, it brings us the benefit of efficiency, the risk of job-losing might be coming near.

Long abstract:

Quite a few international organizations, think tanks, enterprises and other institutions generally believe that AI has a significant and complex impact on employment. Firstly, the emergence of AI will create new job opportunities and positions, the same as the emergence of other new technologies and new equipment in history. The World Labor Organization estimated that the overwhelming impact of generative artificial intelligence GPT-4 would augment jobs rather than automate jobs. INSEAD traced the relationship between the invention of ATMs and employment and believed that generative AI would also increase employment. Secondly, the emergence of AI will replace part of human labor. McKinsey analysts assume that some low-wage jobs are more likely to be affected by the generative AI and face unemployment or job relocation. The impact of AI on employment also varies depending on the different types of economies, job positions, and genders. Relevant data demonstrated that higher-income economies, occupation with the background of algorithmic or clerical-type, and female employees will more likely to be affected.

A case study in AI applied in human resources illustrated the width and depth how it was integrated with service industry. AI performs well in job descriptions, resume screening, interviews, development planning, and even employee relations, which is time saving and energy saving. However, it also proposes the challenge for HR personnels themselves that they have to think how much their job would be substituted by AI.

Traditional Open Panel P112
Transformed social differentiation through digital transformation
  Session 1 Tuesday 16 July, 2024, -