Paper ID
2729
Description
With the rapid advances of artificial intelligence (AI), increasingly more job tasks can be automated. Despite the AI hype, we know little about the extent to which AI may destroy or augment the career of different occupations of professionals. Although most existing literature focused on creating AI automation scores for each occupation, AI may automate non-critical tasks for many occupations which indirectly increases the productivity and value creation of jobs. Therefore, we develop a novel method to estimate the AI automation scores for core and supplemental work activities of all major occupations and analyze how employees’ human capital characteristics may lead to different results: being augmented or displaced by AI. Particularly, skills accumulated from prior work experiences and excellent educational background can reduce the automation risks. Additionally, professionals with major in computing, law, and medicine are more likely to be augmented since only their supplemental work activities may be automated.
Recommended Citation
WANG, QI; Liu, Xuanqi; and Huang, Ke-Wei, "Displaced or Augmented? How does Artificial Intelligence Affect Our Jobs: Evidence from LinkedIn" (2019). ICIS 2019 Proceedings. 28.
https://aisel.aisnet.org/icis2019/economics_is/economics_is/28
Displaced or Augmented? How does Artificial Intelligence Affect Our Jobs: Evidence from LinkedIn
With the rapid advances of artificial intelligence (AI), increasingly more job tasks can be automated. Despite the AI hype, we know little about the extent to which AI may destroy or augment the career of different occupations of professionals. Although most existing literature focused on creating AI automation scores for each occupation, AI may automate non-critical tasks for many occupations which indirectly increases the productivity and value creation of jobs. Therefore, we develop a novel method to estimate the AI automation scores for core and supplemental work activities of all major occupations and analyze how employees’ human capital characteristics may lead to different results: being augmented or displaced by AI. Particularly, skills accumulated from prior work experiences and excellent educational background can reduce the automation risks. Additionally, professionals with major in computing, law, and medicine are more likely to be augmented since only their supplemental work activities may be automated.