Explainability Paths for Sustained Artistic Practice with AI
This addresses the problem of limited explainability in AI tools for artists, but it appears incremental as it builds on existing research-creation practices without introducing a major breakthrough.
The paper tackles the challenge of integrating AI-driven generative audio tools into sustained artistic practice by exploring paths to improve explainability, such as emphasizing human agency over training materials and using small-scale datasets, though no concrete numerical results are provided.
The development of AI-driven generative audio mirrors broader AI trends, often prioritizing immediate accessibility at the expense of explainability. Consequently, integrating such tools into sustained artistic practice remains a significant challenge. In this paper, we explore several paths to improve explainability, drawing primarily from our research-creation practice in training and implementing generative audio models. As practical provisions for improved explainability, we highlight human agency over training materials, the viability of small-scale datasets, the facilitation of the iterative creative process, and the integration of interactive machine learning as a mapping tool. Importantly, these steps aim to enhance human agency over generative AI systems not only during model inference, but also when curating and preprocessing training data as well as during the training phase of models.