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.
Highlights
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
When engaging in a creative process, we normally reflect on what we produce and try to improve according to the feedback that we receive from the outside and to the inner feedback we provide ourselves with
What emerges from the discussion of the last sections is that the measurement of creativity in artificial systems which include CANs is more problematic than the assessment of it in the human and animal systems addressed above (Table 1). This is mainly due to the doubts that arise with respect to the autonomy and naivety of the artificial system
Summary
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. This article belongs to the Topical Collection: Creativity in Art, Science & Mind Guest Editors: Adrian Currie, Anton Killin. 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 worth exploring a way to measure the distance that sets the performance of machine learning systems apart from human creativity, if there is any
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