Towards Machine Learning as an Enabler of Computational Creativity

Abstract

Computational creativity composes a collection of activities that are capable of achieving or simulating behaviors, which can be deemed creative. A frequently articulated criticism for related systems is that the creative capability yet remains with the software designer rather than the computational creative system itself. The rise of machine learning (ML) enables new ways of combining, exploring, and transforming conceptual spaces to achieve creative results. This article demonstrates that the learning occurring within the computational machine through ML enables creative capabilities therein, allowing the computational creative system to be more creative on its own than ever before. Thus, we perceive ML as a key enabler of computational creativity. In this article, we consolidate research from the computer science, computational creativity, and information systems communities, which has been treated separately so far. We build on a framework of human creativity to examine the relationship between creative capabilities and ML mechanisms in ML-based computational creative systems. Specifically, we explicate, which creative capabilities are already established through ML mechanisms in computational creative systems as strengths. Furthermore, we explicate challenges pointing towards further potential of ML-based computational creative systems to enhance the inherent creative capabilities. Our results reveal that ML-based computational creative systems advance the previously static and explicit principles of non-ML-based computational creative systems, yielding creative capabilities on the machine's own, which yet have been in the realm of human actors.Impact Statement—Creativity is a core organizational skill as it enables innovation. With markets becoming increasingly volatile, constant innovation is essential for organizations to create and sustain their economic competitive advantage. Due to the undisputable relevance of creativity for individuals, organizations, and societies, artificial intelligence research has set out to enable creative behavior in computational systems. Especially, ML methods lately became a highly frequented means within computational creative systems. However, while oftentimes applied, ML is seldomly explicitly assessed for its potential to facilitate computational creativity. In this article, we analyze extant computational creative systems relying on ML. The findings serve as a guidance for the design of these systems in various contexts and as pointers for future research to advance the creative capabilities of such systems.

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