Explainable Computational Creativity

Llano, Maria Teresa; d’Inverno, Mark; Yee-King, Matthew; McCormack, Jon; Ilsar, Alon; Pease, Alison; Colton, Simon

doi:10.48550/arxiv.2205.05682

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Relying on explainable AI techniques, a recent field dedicated to investigating the design of computational systems so that the processes are comprehensible to both humans and machines [ 52 ], we propose to study new instances of our solution further. To achieve this, we will build a partially guided artifact, where the user can guide the system and customize the audio–visual associations.…”

Section: Discussionmentioning

confidence: 99%

“Found in Translation”: An Evolutionary Framework for Auditory–Visual Relationships

Rodrigues

Sousa

Cardoso

et al. 2022

Entropy

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The development of computational artifacts to study cross-modal associations has been a growing research topic, as they allow new degrees of abstraction. In this context, we propose a novel approach to the computational exploration of relationships between music and abstract images, grounded by findings from cognitive sciences (emotion and perception). Due to the problem’s high-level nature, we rely on evolutionary programming techniques to evolve this audio–visual dialogue. To articulate the complexity of the problem, we develop a framework with four modules: (i) vocabulary set, (ii) music generator, (iii) image generator, and (iv) evolutionary engine. We test our approach by evolving a given music set to a corresponding set of images, steered by the expression of four emotions (angry, calm, happy, sad). Then, we perform preliminary user tests to evaluate if the user’s perception is consistent with the system’s expression. Results suggest an agreement between the user’s emotional perception of the music–image pairs and the system outcomes, favoring the integration of cognitive science knowledge. We also discuss the benefit of employing evolutionary strategies, such as genetic programming on multi-modal problems of a creative nature. Overall, this research contributes to a better understanding of the foundations of auditory–visual associations mediated by emotions and perception.

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Section: Discussionmentioning

confidence: 99%

“Found in Translation”: An Evolutionary Framework for Auditory–Visual Relationships

Rodrigues

Sousa

Cardoso

et al. 2022

Entropy

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“…If we look at AI in terms of the enabling technology and the culture that surrounds it (Caramiaux and Alaoui 2022), some of the differences and parallels with the use of AI in design become clear. There are common moves to cast AI as a creative partner (Llano et al 2022;McCormack et al 2020) within music, as a solution for optimisation (Noor 2017) within engineering, as a formalisation and purification of human thought (Chiusi 2020; Singh et al 2019) in decision making organisations and so on. Within design, the places that AI might sit are being negotiated.…”

Section: Beyond Education: Nurturing Designerly Ai Culturesmentioning

confidence: 99%

Grasping AI: experiential exercises for designers

Murray-Rust,

Lupetti,

Nicenboim

et al. 2023

AI & Soc

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Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into the functioning of physical and digital products, creating unprecedented opportunities for interaction and functionality. However, there is a challenge for designers to ideate within this creative landscape, balancing the possibilities of technology with human interactional concerns. We investigate techniques for exploring and reflecting on the interactional affordances, the unique relational possibilities, and the wider social implications of AI systems. We introduced into an interaction design course (n = 100) nine ‘AI exercises’ that draw on more than human design, responsible AI, and speculative enactment to create experiential engagements around AI interaction design. We find that exercises around metaphors and enactments make questions of training and learning, privacy and consent, autonomy and agency more tangible, and thereby help students be more reflective and responsible on how to design with AI and its complex properties in both their design process and outcomes.

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“…Long et al (2021) reported different interactive museum exhibits to support creative thinking when learning about different forms of AI technologies. So far, however, advances in co-creative AI tools are limited, as demonstrated by the recent set of principles with which to design explainable computational creativity tools (Llano et al, 2020). Furthermore, there has been no application of digital creativity support or co-creative AI tools in professional sports.…”

Section: Digital Creativity Support and Co-creative Ai Toolsmentioning

confidence: 99%

Designing new digital tools to augment human creative thinking at work: An application in elite sports coaching

et al. 2022

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Creative thinking is desirable in many professions. This article reports new research that followed a design science approach to develop and investigate a co-creative tool called Sport Sparks in one professionthe coaching of professional football players.In response to a coach entering a text description of a coaching challenge (e.g., struggling to maintain the fitness of an athlete) into the tool, the tool automatically generated potentially novel ideas (e.g., reducing game time and changing their nutrition) that the coach could select and/or adapt and evolve into a simple action plan (e.g., which links nutrition to increased game time). This Sport Sparks tool was designed to be an example of human-centred artificial intelligence that aspires to empower humans, to deliver high levels of human control as well as automation, and empower people rather than emulate their expertise. It was engineered with rulebased reasoning to automate the generation of potential new ideas that coaches could select and refine during interactions which provide high user control over this automation. The potential of such a co-creative tool, and value of the guidelines, were demonstrated during the tool's evaluation by coaching practitioners at a Premier League football club. The practitioners used the tool to generate new ideas to coaching challenges, and reported evidence of different forms of creative thinking, although some also reported the need for more support for creative collaborations and solution planning. The paper ends by discussing future directions for both the Sport Sparks tool and other co-creative AI tools.co-creative AI tools, creativity, design guidelines, elite coaching, human-centred AI, sports | INTRODUCTIONCreativity is defined as the ability to produce work that is novel and original, as well as appropriate and useful (Sternberg, 1999). The need for more creative thinking and outcomes is well-documented, for example, to solve complex problems (e.g., Isaksen et al., 2011) and facilitate innovation (e.g., Design Council, 2011). Digital tools to support human creativity and more creative engagement has become one of the means to meet this need (e.g., Candy, 2007). People use these tools to, for example, discover new information, synthesize novel content from existing material,

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Explainable Computational Creativity

Cited by 5 publications

References 2 publications

“Found in Translation”: An Evolutionary Framework for Auditory–Visual Relationships

“Found in Translation”: An Evolutionary Framework for Auditory–Visual Relationships

Grasping AI: experiential exercises for designers

Designing new digital tools to augment human creative thinking at work: An application in elite sports coaching

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