Tuesday, April 7, 2020
Algorithmic Decision-Making: Examining the Interplay of People, Technology, and Organizational Practices through an Economic Experiment
Anh Luong, City University of NY, Baruch College, Zicklin School of Business; City University of New York - Paul H. Chook Department of Information Systems & Statistics, Nanda Kumar, CUNY Baruch College - CIS, Zicklin School of Business, Karl Reiner Lang, City University of New York - Paul H. Chook Department of Information Systems & Statistics identify Algorithmic Decision-Making: Examining the Interplay of People, Technology, and Organizational Practices through an Economic Experiment.
ABSTRACT: Human experts are being increasingly required to work with artificial intelligence and machine learning (AI/ML) in organizational decision-making. Using a large-scale historic dataset, we design and run an economic experiment where financially incentivized participants evaluate loan applications with the aid of an AI/ML. We find that firm performance when employing AI/ML for complex decision-making depends on both reliable technology and well-aligned organizational practices. Human and machine working together can beat the machine operating alone, if there is an incentive alignment mechanism. Most importantly, when both high accuracy AI/ML and well-aligned incentive structures are put into place, firms maximize their profits. We also find that, only when the AI/ML in use has adequate accuracy can the human-machine team excel humans operating on their own. Otherwise, with a sub-par accuracy AI/ML, even when it is significantly better than chance, humans are better off working alone. We contribute to the emerging algorithmic decision-making literature by introducing the incentive alignment perspectives. To the best of our knowledge, we are the first in this area to examine the impacts of both AI/ML systems’ properties and organizational practices, in addition to accounting for the characteristics of human decision makers. Importantly, we examine and highlight the importance of their interdependent effect on maximizing organizational outcomes. We also contribute to the literature investigating which tasks should and should not be automated. Our comparison between man-machine collaboration and machine alone is especially important given rising concerns about automation replacing humans and exacerbating inequality. Our findings hold implications for firms wishing to build sustainable human-machine collaboration, that not only serves to increase organizational financial gains, but more importantly, also to solidify the role of humans in the current employment landscape that is constantly changing due to the rapid advances in AI/ML every day.