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Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry
This article presents a five-year collaboration situated at the intersec...
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Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our li...
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Artificial Intelligence and Statistics
Artificial intelligence (AI) is intrinsically data-driven. It calls for ...
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OnionBot: A System for Collaborative Computational Cooking
An unsolved challenge in cooking automation is designing for shared kitc...
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How Much Automation Does a Data Scientist Want?
Data science and machine learning (DS/ML) are at the heart of the recent...
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A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology
Artificial intelligence (AI) enabled radiomics has evolved immensely esp...
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An Interaction Framework for Studying Co-Creative AI
Machine learning has been applied to a number of creative, design-orient...
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Machinic Surrogates: Human-Machine Relationships in Computational Creativity
Recent advancements in artificial intelligence (AI) and its sub-branch machine learning (ML) promise machines that go beyond the boundaries of automation and behave autonomously. Applications of these machines in creative practices such as art and design entail relationships between users and machines that have been described as a form of collaboration or co-creation between computational and human agents. This paper uses examples from art and design to argue that this frame is incomplete as it fails to acknowledge the socio-technical nature of AI systems, and the different human agencies involved in their design, implementation, and operation. Situating applications of AI-enabled tools in creative practices in a spectrum between automation and autonomy, this paper distinguishes different kinds of human engagement elicited by systems deemed automated or autonomous. Reviewing models of artistic collaboration during the late 20th century, it suggests that collaboration is at the core of these artistic practices. We build upon the growing literature of machine learning and art to look for the human agencies inscribed in works of computational creativity, and expand the co-creation frame to incorporate emerging forms of human-human collaboration mediated through technical artifacts such as algorithms and data.
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