Explainability Paths for Sustained Artistic Practice with AI

TL;DR

This paper explores methods to improve explainability in AI generative audio tools to better support sustained artistic practice, emphasizing human agency and interactive machine learning.

Contribution

It introduces practical paths for enhancing explainability in generative audio AI, focusing on human control over training, datasets, and iterative creative processes.

Findings

Human agency over training materials enhances explainability.

Small-scale datasets are viable for training generative models.

Interactive machine learning facilitates better understanding and control.

Abstract

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.