AI in Business and Society
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Paper Number
1573
Paper Type
Completed
Description
In today’s organizations, both humans and machine learning (ML) systems jointly form routines. Yet, we do not know much about the underlying reciprocal interplay between them, which complicates their effective coordination. Taking an organizational learning perspective, we study the dynamics of human learning and ML to understand how organizations can benefit from their respective idiosyncrasies when enabling bilateral learning. Drawing on a case of human traders and a reinforcement ML system trading productively at Allianz Global Investors, we apply human-machine pattern recognition on digital trace data to explore their (interconnected) dynamics. We find that bilateral learning can increase trading performance, which appears to result from an emerging virtuous cycle between humans and the ML system. Our explorative case study offers insights into how organizational learning depends on the coordination of both human learning and ML, which can help manage the collaboration between human and artificial intelligence within organizational routines.
Recommended Citation
Sturm, Timo; Koppe, Timo; Scholz, Yven; and Buxmann, Peter, "The Case of Human-Machine Trading as Bilateral Organizational Learning" (2021). ICIS 2021 Proceedings. 3.
https://aisel.aisnet.org/icis2021/ai_business/ai_business/3
The Case of Human-Machine Trading as Bilateral Organizational Learning
In today’s organizations, both humans and machine learning (ML) systems jointly form routines. Yet, we do not know much about the underlying reciprocal interplay between them, which complicates their effective coordination. Taking an organizational learning perspective, we study the dynamics of human learning and ML to understand how organizations can benefit from their respective idiosyncrasies when enabling bilateral learning. Drawing on a case of human traders and a reinforcement ML system trading productively at Allianz Global Investors, we apply human-machine pattern recognition on digital trace data to explore their (interconnected) dynamics. We find that bilateral learning can increase trading performance, which appears to result from an emerging virtuous cycle between humans and the ML system. Our explorative case study offers insights into how organizational learning depends on the coordination of both human learning and ML, which can help manage the collaboration between human and artificial intelligence within organizational routines.
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