Stakeholder Groups in Computational Creativity Research and Practice

Abstract

Abstract. The notion that software could be independently and usefully creative is becoming more commonplace in scientific, cultural, business and public circles. It is not fanciful to imagine creative software embedded in society in the short to medium term, acting as collaborators and autonomous creative agents for much societal benefit. Technologically, there is still some way to go to enable Artificial Intelligence methods to create artefacts and ideas of value, and to get software to do so in interesting and engaging ways. There are also a number of sociological hurdles to overcome in getting society to accept software as being truly creative, and we concentrate on those here. We discuss the various communities that can be considered stakeholders in the perception of computers being creative or not. In particular, we look in detail at three sets of stakeholders, namely the general public, Computational Creativity researchers and fellow creatives. We put forward various philosophical points which we argue will shape the way in which society accepts creative software. We make various claims along the way about how people perceive software as being creative or not, which we believe should be addressed with scientific experimentation, and we call on the Computational Creativity research community to do just that.

A Blackboard System for Generating Poetry

“…In order to make a comparative evaluation, we used Ritchie's empirical criteria [39] to evaluate the outputs and compared our results with those presented by Pablo Gervas and Francisco Pereira et al [18,37] for the WASP poetry generator. We also evaluated the creative behavior of our system by incorporating the FACE model [9].…”

“…FACE [9] provides one such model of evaluation, which we applied to our system. A creative act that satisfies all FACE criteria is denoted by a tuple F g , A g , C g , E g , where the C (concept) means the system taking input and producing outputs denoted by E (expressions), the A (aesthetic measure) is the fitness function evaluating the (concept, expression) pairs with real-number values, and the F (framing information) is the linguistic comment explaining the context or motivation of the outputs.…”

“…However, in the other approach, it is argued that the process of creation should also be considered in conjunction with the characteristics of the outputs [9]. Consequently, these approaches to computational creativity focus on modeling the processes by which creative artifacts are produced; for example, by incorporating the intentions of the artist [10].…”

“…Consequently, these approaches to computational creativity focus on modeling the processes by which creative artifacts are produced; for example, by incorporating the intentions of the artist [10]. Some frameworks for evaluating the creativity of computational systems have also been proposed [9,25,39].…”

Data mining and machine learning in computational creativity

“…These and other patterns mined from text documents can then be used in creative tasks such as metaphor generation (‘he is a bull’) or conceptual blending . Such creative functions can be used, for instance, in the generation of poetry …”