Mandated Vs. Autonomous AI Use: An Empirical Examination On Worker’s Labor Outcomes And Social Welfare

Project Description

Due to AI’s computation power, scalability, and cost efficiencies, algorithmic management (i.e., the managerial role played by AI) has become popular. Such mandated AI use provides benefits to the workers. This includes, but is not limited to, convenience, acquiring workplace skills, and improved productivity. However, mandated AI use also brings a critical challenge to the workers: the loss of human autonomy. While significant academic and practice debate has surrounded the AI use mandate, limited investigation has been conducted to uncover how such mandated AI use affects workers’ job performance and autonomy, as well as its impact on firm-level and societal level benefits.

In response to this gap, this project aims to measure the impact of mandated vs. autonomous AI use on empowerment and labor outcomes. We also aim to quantify the impact of mandated vs. autonomous AI use on platform-level and societal welfare (e.g., fairness and safety). To investigate these questions, we focus on the context of food delivery platforms which are at the forefront of developing and applying various AI applications as well as at the center of ongoing debates on the mandated use of AI. We obtained data from a randomised field experiment with two different modes of AI use (Human 100%, AI 100%, and Human-AI).

Research Technical Area

  • AI usage and adoption
  • Ethical AI design
  • Human behavior change

Benefits to the society

This project helps social planners to design effective and ethical AI adoption strategies through evidence from field experiments and in-depth surveys.

This project provides proof of concepts of AI-Human collaboration and design guidelines on how to adopt AI to spur workers toward better productivity and empowerment.

The validation of the effectiveness of different AI use modes (i.e., mandatory vs. autonomous AI use) by this work helps social planners and the government to allocate AI resources more appropriately.

Project’s Publications

Team’s Principal Investigator

Nakyung KYUNG

Nakyung obtained her Ph.D. in Management Engineering from the Korea Advanced Institute of Science and Technology (KAIST). Nakyung currently works as an assistant professor at the National University of Singapore. Nakyung’s research is grounded on the intermediate integration of Information systems (IS), economics, and well-being. Her research streams include behavior change, healthcare analytics, and mobile business analytics. She is also interested in the societal effects of AI. Specifically, ethical issues related to AI and the side effects of AI are key research interests. Within these domains, she has employed quantitative methodologies, including big data analytics using actual behavioral data, econometrics, and field experiments.

The Team


Assoc. Professor Jason CHAN, University of Minnesota (School of Management)
Research Focus: Design of Emerging IT, Social Impact of AI, IT Ethics