Shelley: A Crowd-sourced Collaborative Horror Writer

Yanardag, Pinar; Cebrián, Manuel; Rahwan, Iyad

doi:10.1145/3450741.3465251

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…While earlier generations of digital creativity support tools relied primarily on interactive techniques to encourage human creativity, more recent co-creative AI tools deploy different forms of machine reasoning. As reported earlier, for example, Calliope deployed generative design algorithms to search large possible design spaces (Davis et al, 2021), and Shelley implemented deep-learning algorithms to generate horror stories (Yanardag et al, 2021). Elsewhere, Karimi et al (2019) reported a co-creative design system that used a computational model of conceptual shift based on clustering of deep features from a database of sketches, and the JECT.AI tool implemented creative search algorithms to retrieve news content that journalists could use to discover new angles on stories (Maiden, Zachos, Brown, et al, 2020).…”

Section: Digital Creativity Support and Co-creative Ai Toolsmentioning

confidence: 99%

“…The resulting Sport Sparks tool built on an earlier proof-of-concept, and was designed and implementing using guidelines for the first two of these stagesanalysing the problem and generating ideas. The result was a tool that sought to situate machine reasoning in-the-loop around, in this case, coaching practitioners, rather than continue the human-in-the-loop collaborations reported for co-creative AI tools such as Calliope (Davis et al, 2021) and Shelley (Yanardag et al, 2021).…”

Section: The Research Questions Revisitedmentioning

confidence: 99%

“…Recent publications have revealed that more of these tools are leveraging artificial intelligence algorithms. Examples of these tools include Calliope, a virtual reality system that enabled users to explore and manipulate generative design solutions in real time (Davis et al, 2021), and Shelley, a deep-learning horror writing engine deployed as a Twitter bot to crowd-source human inputs (Yanardag et al, 2021). These tools are often referred to as co-creative AI tools (e.g., Long et al, 2021).…”

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confidence: 99%

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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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Section: Digital Creativity Support and Co-creative Ai Toolsmentioning

confidence: 99%

Section: The Research Questions Revisitedmentioning

confidence: 99%

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confidence: 99%

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

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“…mobilize both individual and collective action. In a related research direction, GANs have been used for humanitarian applications, for instance to increase empathy for victims of far-away disasters by transforming landmarks in major cities as if they were impacted by war (Yanardag et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Simulated Data to Generate Images of Climate Change

Cosne,

Juraver,

Teng

et al. 2020

Preprint

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Generative adversarial networks (GANs) used in domain adaptation tasks have the ability to generate images that are both realistic and personalized, transforming an input image while maintaining its identifiable characteristics. However, they often require a large quantity of training data to produce high-quality images in a robust way, which limits their usability in cases when access to data is limited. In our paper, we explore the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming to use the resulting images to raise awareness of the potential future impacts of climate change.

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Bots and recommender systemsBots are becoming pervasive in our social media. From Twitter to Reddit, bots can interact with humans without detection, influencing opinions, and creating artificial narratives (Hurtado et al 2019;Yanardag et al 2021). This study uses an agent-based

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confidence: 99%

Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems

2022

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Bots’ ability to influence public discourse is difficult to estimate. Recent studies found that hyperpartisan bots are unlikely to influence public opinion because bots often interact with already highly polarized users. However, previous studies focused on direct human-bot interactions (e.g., retweets, at-mentions, and likes). The present study suggests that political bots, zealots, and trolls may indirectly affect people’s views via a platform’s content recommendation system's mediating role, thus influencing opinions without direct human-bot interaction. Using an agent-based opinion dynamics simulation, we isolated the effect of a single bot—representing 1% of nodes in a network—on the opinion of rational Bayesian agents when a simple recommendation system mediates the agents’ content consumption. We compare this experimental condition with an identical baseline condition where such a bot is absent. Across conditions, we use the same random seed and a psychologically realistic Bayesian opinion update rule so that conditions remain identical except for the bot presence. Results show that, even with limited direct interactions, the mere presence of the bot is sufficient to shift the average population’s opinion. Virtually all nodes—not only nodes directly interacting with the bot—shifted towards more extreme opinions. Furthermore, the mere bot’s presence significantly affected the internal representation of the recommender system. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence population opinions not only by directly interacting with humans but also by secondary effects, such as shifting platforms’ recommendation engines’ internal representations. The mediating role of recommender systems creates indirect causal pathways of algorithmic opinion manipulation.

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Shelley: A Crowd-sourced Collaborative Horror Writer

Cited by 14 publications

References 16 publications

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

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

Using Simulated Data to Generate Images of Climate Change

Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems

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