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Explainable Computational Creativity Explainable Computational Creativity Maria Teresa Llano Mark d’Inverno Matthew Yee-King SensiLab, Monash University Goldsmiths, University of London Goldsmiths, University of London Melbourne, Australia London, United Kingdom London, United Kingdom Jon McCormack Alon Ilsar Alison Pease Simon Colton SensiLab, Monash University SensiLab, Monash University Dundee University SensiLab, Monash University Melbourne, Australia Melbourne, Australia Dundee, United Kingdom Queen Mary, University of London Abstract for intermediate explanations as the process progresses, let Human collaboration with systems within the Computational alone place for exchanges of information that can exploit Creativity (CC) field is often restricted to shallow interac- human-machine co-creation. tions, where the creative processes, of systems and humans alike, are carried out in isolation, without any (or little) inter- In this paper we propose Explainable Computational Cre- vention from the user, and without any discussion about how ativity (XCC) as a subfield of XAI. The focus of this subfield the unfolding decisions are taking place. Fruitful co-creation is the study of bidirectional explainable models in the con- requires a sustained ongoing interaction that can include dis- text of computational creativity – where the term explainable cussions of ideas, comparisons to previous/other works, in- is used with a broader sense to cover not only one shot-style cremental improvements and revisions, etc. For these interac- explanations, but also for co-creative interventions that in- tions, communication is an intrinsic factor. This means giving volve dialogue-style communications. More precisely, XCC a voice to CC systems and enabling two-way communication investigates the design of CC systems that can communicate channels between them and their users so that they can: ex- and explain their processes, decisions and ideas through- plain support their processes and decisions, their ideas so that out the creative process these are given serious consideration by their creative collab- in ways that are comprehensible to orators, and learn from these discussions to further improve both humans and machines. The ultimate goal of XCC is to their creative processes. For this, we propose a set of design open up two-way communication channels between humans principles for CC systems that aim at supporting greater co- and CC systems in order to foster co-creation and improve creation and collaboration with their human collaborators. the quality, depth and usefulness of collaborations between them. Introduction Designing and implementing this type of communication Although systems from the field of Computational Creativ- is extremely complex; however, we believe it is important to ity (which we will refer to from now on as CC systems) start a discussion towards what is needed for more fruitful – and more generally AI systems – have been successful and productive partnerships between CC systems and their in different application domains, a common challenge for users. Creativity is not a solo act, it is a social activity that users and researchers is that most of these systems behave as benefits from life experiences, from influences of people, black boxes, limited to opaque interactions where processes and from the contributions of different collaborators. This and reasoning are completely unknown or obscure (Scherer is why we are interested here in co-creative CC systems and 2016). As a result, users are left questioning the nature of on addressing the lack of two-way channels of communica- the system’s decisions, or are discouraged to the notion of tion. This may result in increased novelty, or higher-value collaborating with them. These limitations have raised the creative collaborations, however it is difficult to anticipate need for the development of models that offer more clar- how successful and in which way any creative collabora- ity and transparency in order to improve the potential for tion can emerge, but we believe the kind of active collab- human-machine interactions with CC systems (Muggleton orations proposed through XCC would ultimately enhance et al. 2018; Bryson and Winfield 2017). the human-machine collaboration experience and increase The field of Explainable AI (XAI) has grown in recent the engagement of users with CC systems. years with the goal of making black box systems more trans- parent and accountable through models of explanation that In the rest of the paper, we first provide relevant back- communicate the way decisions have been reached. Current ground literature. Then we present an overview of the cur- approaches to XAI, which are commonly associated with rent state of the art of co-creation and explainable AI in gen- popular but opaque machine learning methods, centre on eral and in CC in particular. We follow by identifying de- providing explanations as part of the output of the system; sign principles that we believe CC systems with explainable i.e. the focus is on delivering a final result to a user alongside capabilities should have. We finish with a discussion outlin- a rationale of how this result was created. However, the cre- ing challenges that need to be considered, opportunities that ative process is often performed in isolation, with no place may arise, conclusions and directions for future work. Proceedings of the 11th International Conference on Computational Creativity (ICCC’20) 334 ISBN: 978-989-54160-2-8 plays that role by explaining how the system works; i.e. how he designed the system. For instance: Designer: It has a kind of reset function that causes it to forget your patterns occasionally.” Human musician: Yes I did wonder as sometimes it seemed to have logic in how it responded and then at other times it didn’t make sense to me.” Even though the algorithm designer provides information about how the system operates, he is unable to provide an explanation of what was going on at specific points in the Figure 1: The set of annotations created during the evalua- performance as only the system “knows” the details of what tion of SpeakeSystem. At the top of the image is the audio went on at every point of the performance. timeline. The coloured boxes below represent the annota- If the SpeakeSystem had been equipped with communi- tions created by each of three annotators. Here the system cation capabilities, not only it could have provided insights cannot contribute to the conversation; i.e. the system does about its creative process in the conversation above, but the not have a voice. human musician could have communicated his intentions during the performance (perhaps through some kind of vi- sual or haptic signal), giving the system a chance to respond Motivation and Related Work during co-creation. This could have resulted in a more en- Co-creation, real-time interactions and collaboration are im- gaging experience, for the musician and the audience alike. portant topics in the CC community; however, the communi- cation between CC systems and their users is limited to a few Co-creation, Real-Time Interactions and exchanges, or the rationale behind their individual actions is Collaboration in CC often unknown by either, or there is little or no opportunity for discussion of the ideas presented, and many system out- Models to improve collaboration and co-creation within CC puts are discarded without a second thought. have been explored in the community. In (Davis et al. 2015), Take for instance the SpeakeSystem: a real-time inter- for instance, an enactive model of creativity is proposed for active music improviser which takes as its input an audio collaboration and co-creation. The key principle for this stream from a monophonic instrument, and produces as its model is that the system has a level of awareness and that output a sequence of musical note events which can be used co-creation happens through a real-time and improvised in- to control a synthesizer. The BBC Radio 3 Jazz Line Up pro- teraction with the environment and other agents. Another gramme commissioned SpeakeSystem in 2015 for a live hu- approach is mixed-initiative co-creativity (Yannakakis, Li- man and computer performance with alto saxophone player apis, and Alexopoulos 2014), which exploits a bi-directional Martin Speake12. Figure 1 shows a recording of the perfor- communication based on the collective exploration of the mance that was uploaded to a timeline annotation system in design space and human lateral decisions that are used by order to carry out a focused discussion about it. The conver- the system to guide the creative task. Although these mod- sation involved the musician, the algorithm designer and a els establish communication channels, these are limited to member of the audience. However, a key participant of the an action/reaction type of model; i.e. there is little oppor- performance was not involved, the interactive music impro- tunity for further introspection in addition that they do not viser. To illustrate, at some point of the recording the human enable the systems to further support their contributions. musician commented: The You Can’t Know my Mind installation of The Paint- ing Fool (Colton and Ventura 2014) presented real-time in- “I think by this stage that I wanted the algorithm to teractions with users that resembled some of the aspects we come up with a new interaction mode.” cover in this paper: the system provides explanations about while an audience member said: its process, motivations, etc. (e.g. by providing a commen- “I wonder what you are both thinking going into this tary alongside its output), the user provides content to guide section. The algorithm not a lot I suspect! Otherwise it the creative process (e.g. by expressing a particular emotion would play notes.” to be the inspiration for the painting), and the system utilizes visual cues to reveal what is going on (e.g. with the use of an In a human-human interaction, the musician being ‘ques- on-screen hand while it paints the picture).
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