This article explores the relationship and disparities between human and computational creativity by addressing the following questions: How well are computational creativity systems currently performing at creative tasks? Could computers outperform human composers? And, if not, is computational creativity a utopia? Automatic composition systems are examined with respect to Boden’s three criteria of creativity (novelty, surprise and value), as well as their assumptions about the nature of creativity. As an alternative to a competitive relationship between human and computational creativity, the article proposes the concept of a distributed human–computer co-creativity, in which computational creativity extends – rather than replaces – human creativity, by expanding the space of creative possibilities.

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This paper describes a subversive compositional approach to machine learning, focused on the exploration of AI bias and computational aesthetic evaluation. In Bias, for bass clarinet and Interactive Music System, a computer music system using two Neural Networks trained to develop "aesthetic bias" interacts with the musician by evaluating the sound input based on its "subjective" aesthetic judgments. The composition problematizes the discrepancies between the concepts of error and accuracy, associated with supervised machine learning, and aesthetic judgments as inherently subjective and intangible. The methods used in the compositional process are discussed with respect to the objective of balancing the trade-off between musical authorship and interpretative freedom in interactive musical works.