Essays on the impact of robots and AI on the labor market and healthcare

Abstract

As artificial intelligence and robotics advance, there is an increasing effort to apply it to work processes to increase labor productivity. Along with efforts to adapt new advanced technologies to industry, we need to examine the changes they will bring. Therefore, this dissertation studies the changes in the labor market that occur in the process along with methods of using artificial intelligence and robotics. The first essay explores the changes in the labor market as industrial robots are introduced into manufacturing. We propose that robots are broadening the scope of automation by showing empirically that non-routine manual tasks are being replaced based on task-based frameworks. In the second essay, we suggest personalization in the healthcare field using a deep learning model. We build a model that provides the optimal drug for each patient after estimating individual treatment effects by applying deep learning-based counterfactual regression to real-world patient data. In the third essay, we research how to distinguish long-term changes by applying clustering to sequential data. In this essay, we apply explainable machine learning to provide clinical implications by identifying important variables that can differentiate clustered groups. This thesis provides policy implications by examining changes that occur in the labor market when new technologies are introduced, as well as implications for investigating how artificial intelligence can be applied to companies using real-world data.