# Explainability Paths for Sustained Artistic Practice with AI

arXiv: https://arxiv.org/abs/2407.15216 · HTML: https://mlsanity.com/paper/2407.15216/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [RAVE: A variational autoencoder for fast and high-quality neural audio synthesis](https://mlsanity.com/paper/2111.05011/) (2021)
- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
- [Generating Albums with SampleRNN to Imitate Metal, Rock, and Punk Bands](https://mlsanity.com/paper/1811.06633/) (2018)

## Built on by (most cited first, 4 of 4)

- [XAIxArts Manifesto: Explainable AI for the Arts](https://mlsanity.com/paper/2502.21220/) (2025)
- [Explainability-in-Action: Enabling Expressive Manipulation and Tacit Understanding by Bending Diffusion Models in ComfyUI](https://mlsanity.com/paper/2508.07183/) (2025)
- [Why Open Small AI Models Matter for Interactive Art](https://mlsanity.com/paper/2511.09788/) (2025)
- [Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability](https://mlsanity.com/paper/2607.22428/) (2026)

## Abstract (arXiv)

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.