Since 2022, Management
My research interests lie at the intersection of AI, creativity, and organizations, with a focus on how individuals can leverage AI to improve creative and business outcomes. As an AI social scientist, I study how to best use AI to augment managerial and knowledge work as well as when it is appropriate to delegate tasks to AI automation and when it is apt to rely on human knowledge for judgements. I also examine the potential for AI to contribute to the theme of computational creativity by streamlining the creative process, decomposing it into logical sequences, and simulating it - as is the case with generative AI technologies. I use a range of quantitative methods, including machine learning, econometric analysis, and experiments, to study these topics.
In one project, I examine the potential for AI explanations to reduce biases in AI-augmented human judgments. In another project, I use causal machine learning to investigate how creative achievement shapes an artist's creative trajectory, including their decision-making in terms of artistic positioning (such as novelty versus nostalgia), sonic characteristics, and category spanning (for example, which genres, moods, or markets to enter), as well as collaboration choices. In the future, I plan to conduct field experiments to examine the effects of AI-human collaboration on creative works.