A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative

Computational creativity is a flourishing research area, with a variety of creative systems being produced and developed. Creativity evaluation has not kept pace with system development with an evident lack of systematic evaluation of the creativity of these systems in the literature. This is partially due to difficulties in defining what it means for a computer to be creative; indeed, there is no consensus on this for human creativity, let alone its computational equivalent. This paper proposes a Standardised Procedure for Evaluating Creative Systems (SPECS). SPECS is a three-step process: stating what it means for a particular computational system to be creative, deriving and performing tests based on these statements. To assist this process, the paper offers a collection of key components of creativity, identified empirically from discussions of human and computational creativity. Using this approach, the SPECS methodology is demonstrated through a comparative case study evaluating computational creativity systems that improvise music

Implementing feedback in creative systems : a workshop approach

One particular challenge in AI is the computational modelling and simulation of creativity. Feedback and learning from experience are key aspects of the creative process. Here we investigate how we could implement feedback in creative systems using a social model. From the field of creative writing we borrow the concept of a Writers Workshop as a model for learning through feedback. The Writers Workshop encourages examination, discussion and debates of a piece of creative work using a prescribed format of activities. We propose a computational model of the Writers Workshop as a roadmap for incorporation of feedback in artificial creativity systems. We argue that the Writers Workshop setting describes the anatomy of the creative process. We support our claim with a case study that describes how to implement the Writers Workshop model in a computational creativity system. We present this work using patterns other people can follow to implement similar designs in their own systems. We conclude by discussing the broader relevance of this model to other aspects of AI

Do creative industries cluster? Mapping Creative Local Production Systems in Italy

An important debate on the role of creativity and culture as factors of local economic development is distinctly emerging. Despite the emphasis put on the theoretical definition of these concepts, it is necessary to strengthen comparative research for the identification and analysis of the kind of creativity embedded in the territory as well as its determinants. Creative local production systems are identified in Italy and Spain departing from local labour markets as territorial units, and focusing on two different kinds of creative industries: traditional cultural industries (publishing, music, architecture and engineering, performing arts) and technology-related creative industries (R&D, ICT, advertising). The results suggest the existence of different patterns of concentration of creative industries in both countries and the concentration of creative industries in thecreative industries, creative local systems, agglomeration economies

Why do creative industries cluster? An analysis of the determinants of clustering of creative industries

Creative industries tend to concentrate mainly around large- and medium-sized cities, forming creative local production systems. The text analyses the forces behind clustering of creative industries to provide the first empirical explanation of the determinants of creative employment clustering following a multidisciplinary approach based on cultural and creative economics, evolutionary geography and urban economics. A comparative analysis has been performed for Italy and Spain. The results show different patterns of creative employment clustering in both countries. The small role of historical and cultural endowments, the size of the place, the average size of creative industries, the productive diversity and the concentration of human capital and creative class have been found as common factors of clustering in both countries.creative industries, creative local production systems, creative clusters, agglomeration economies

Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems

Is it Time for Computational Creativity to Grow Up and Start being Irresponsible?

A recent definition of computational creativity has em- phasised that computational creativity systems should “take on certain responsibilities” for generating creative behaviour. This paper examines the notion of responsibilities in that definition, and looks at a number of aspects of the creative act and its context that might play a role in that responsibility, with an emphasis on artistic and musical creativity. This problematises the seemingly simple distinction between systems that have responsibilities for creative activity and those which support or provide tools for creativity. The paper con- cludes with a discussion of an alternative approach to the subject, which argues that the responsibility for creative action is typically diffused through a complex human/computer system, and that a “systems thinking” approach to locating computational creativity might ask better questions than one that tries to pin creative responsibility to a particular agent

Creative methodologies for understanding a creative industry

The chapter presents a conceptual framework for the identification and analysis of value creating and value capture systems within creative industry contexts based on theoretical and empirical studies. It provides a ‘digital economy’ perspective of the creative industries as a micro-level example of a wider analytical problem, which is how society changes itself. The increasing level of innovation and creativity produces greater levels of instability in social structures (habits, norms etc.) Completely new industries can arise (and ‘creatively’ destroy old ones) as new stabilised patterns form, particularly where entry costs are tumbling, such as digital milieu. Observations of workshops over several days with creative groups, interviews with creative enterprises, literature reviews on creative industries, business models and value systems have informed the analysis and conceptualisation. As a result we present a conceptual framework that we suggest can capture how novelty arises as emergent order over time. We have extended previous work that investigates the significance of emergence in theorising entrepreneurship into an exploration of how to articulate the creation and flow of value and effective ontology in a creative landscape. In the digital economy, the creative industries revolve around dynamic, innovative and often unorthodox collaborations, whereby numerous large, small and micro-businesses come together for the duration of a project, then disband and form new partnerships for the next project. Research designs must therefore address multiple contexts and levels presenting an analytical challenge to researchers. Methodologically, we suggest that the framework has analytical potential to support the collection of data: ordering and categorising empirical observations concerning how different phenomena emerge over time across multiple levels of analysis and contexts. Conceptually, the work broadens the notions of ‘business model’ to consider value creating systems and particular states reached by those systems in their evolution. The work contributes new concepts for researchers in this field and a wider framework for practitioners and policy makers