Twenty-fourth International Conference on Knowledge, Culture, and Change in Organizations

Abstract

On March 9th of 2016, AlphaGo defeated Lee Sedol, a player who had won the world Go championship 14 times. This represented another victory of artificial intelligence (AI) over human intelligence on intelligent games after chess. This research aims to study the interactions between humans and AI, where human’s expertise is challenged by the machine learning’s capability with the case of AlphaGo and Go community. This project ethnographically examines the following questions: How do Go players and the professional Go community understand the black box of machine learning? How did the credibility of the machine learning algorithm, AlphaGo, develop during and following its introduction in a challenge match against a world-class Go player? How is the Go community changing in response to the machine learning programs, including in changes in Go theory, training, protocols for managing cheating, etc.? How are human Go players understanding and learning Go knowledge produced by Go AI? The goal of the research is to answer these questions with empirical studies on the practices of Go community in the post-AlphaGo era, which could provide some insights on understanding the interactions between human communities and AI, especially with the explosion of large language models.