Author(s) :

Joonha Park.

Volume/Issue :

Abstract :

Artificial intelligence (AI) has been reshaping how work is performed across states, while creating changes for both growth and concerns about job loss. Leaders are currently in a situation where they need to make a clear yet fair rule to support innovation, while preserving jobs for workers. This study particularly asks whether it is possible for a simple, single dataset-based rule can help leaders balance artificial intelligence with job preservation. This study hypothesized that a rule rewarding higher artificial intelligence task exposure with gentle penalty on extreme exposure would keep the national portfolio clustered at a national mean, while maintaining priorities on high-potential places. Using one publicly available dataset with the score of exposure to artificial intelligence from the Brookings Institution (standardized state artificial intelligence exposure, 2017), this study analyzed 50 states with the District of Columbia. Exposure scores were calculated to have the average of -0.007 (SD 0.044), with Hawaii the lowest (-0.115) and Indiana the highest (0.065). According to ± 1 standard deviation bands, this study classified 10 innovation-max states, 33 balanced states, and 8 job-preservation support states. With an equal-weight index of a=0.5 to rank states, the top five states were Indiana, Kentucky, Michigan, the District of Columbia, and Washington. This ranking stayed unchanged when shifting weights towards innovation (a=0.7) or to job protection (a=0.3) through sensitivity analysis. Results in this study supported the hypothesis in this study about how most states remained clustered around the center, while maintaining priorities on high-potential places. The method taken in this study was transparent, offering a practical initiative for policy design.