# An Autoethnographic Exploration of XAI in Algorithmic Composition

arXiv: https://arxiv.org/abs/2308.06089 · HTML: https://mlsanity.com/paper/2308.06089/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Learning to Traverse Latent Spaces for Musical Score Inpainting](https://mlsanity.com/paper/1907.01164/) (2019)
- [The Machine Learning Algorithm as Creative Musical Tool](https://mlsanity.com/paper/1611.00379/) (2016)
- [Exploring XAI for the Arts: Explaining Latent Space in Generative Music](https://mlsanity.com/paper/2308.05496/) (2023)

## Built on by (most cited first, 1 of 1)

- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)

## Abstract (arXiv)

Machine Learning models are capable of generating complex music across a range of genres from folk to classical music. However, current generative music AI models are typically difficult to understand and control in meaningful ways. Whilst research has started to explore how explainable AI (XAI) generative models might be created for music, no generative XAI models have been studied in music making practice. This paper introduces an autoethnographic study of the use of the MeasureVAE generative music XAI model with interpretable latent dimensions trained on Irish folk music. Findings suggest that the exploratory nature of the music-making workflow foregrounds musical features of the training dataset rather than features of the generative model itself. The appropriation of an XAI model within an iterative workflow highlights the potential of XAI models to form part of a richer and more complex workflow than they were initially designed for.