Measuring creativity: an account of natural and artificial creativity
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(2021) 11:1
PAPER IN GENERAL PHILOSOPHY OF SCIENCE
Open Access
Measuring creativity: an account of natural and artificial creativity Caterina Moruzzi 1 Received: 14 September 2019 / Accepted: 15 September 2020/ # The Author(s) 2020
Abstract Despite the recent upsurge of interest in the investigation of creativity, the question of how to measure creativity is arguably underdiscussed. The aim of this paper is to address this gap, proposing a multidimensional account of creativity which identifies problem-solving, evaluation, and naivety as measurable features that are common among creative processes. The benefits that result from the adoption of this model are twofold: integrating discussions on creativity in various domains and offering the tools to assess creativity across systems of different kinds. By situating creativity within this framework, I aim to contribute to a non-anthropocentric, more comprehensive understanding of the notion, and to debates on natural and artificial creativity. Keywords Creativity . Problem solving . Naivety, evaluation . Computational creativity .
CANs . New Caledonian crows . Kekulé
1 Introduction Many definitions of creativity arguably tend toward anthropocentric conceptions, associating it with features such as value, flair (Gaut 2018), intuition, and impact on the wider society (Kaufman and Beghetto 2009), in addition to novelty. Some have recognized the need for a more inclusive and less human-centric account of creativity in order to avoid disciplinary biases and facilitate a broader understanding of this concept (Jordanous and Keller 2016). Indeed, a lack of commonly-agreed interpretations of creativity hinders our ability to assess and evaluate the creativity possessed by different systems and to compare them on the basis of their creative capacities (Jordanous 2011).
This article belongs to the Topical Collection: Creativity in Art, Science & Mind Guest Editors: Adrian Currie, Anton Killin
* Caterina Moruzzi [email protected]; [email protected]
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Università degli Studi di Torino, via Verdi, 8, 10124 Torino, TO, Italy
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European Journal for Philosophy of Science
(2021) 11:1
The technological revolution that we are living makes this necessity even more urgent. The rapid development of machine learning systems in the last decades and the improvement of their performance in multiple fields provoked an upsurge of debates regarding whether these systems can reach human-level performance and, if so, what would distinguish us from them. It is thus worth exploring a way to measure the distance that sets the performance of machine learning systems apart from human creativity, if there is any. In this paper I aim to address this need by suggesting a framework to situate and assess instances of natural and artificial creativity that does not rely on external factors but only on the inner structure of the creative system itself. As a result, this model can be usefully applied to analyze disputed instances of creativity, for example when discus
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