# Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge

arXiv: https://arxiv.org/abs/2004.06894 · HTML: https://mlsanity.com/paper/2004.06894/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

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

- [Towards a Rigorous Evaluation of Explainability for Multivariate Time Series](https://mlsanity.com/paper/2104.04075/) (2021)

## Abstract (arXiv)

Interpretability of machine learning models has gained more and more attention among researchers in the artificial intelligence (AI) and human-computer interaction (HCI) communities. Most existing work focuses on decision making, whereas we consider knowledge discovery. In particular, we focus on evaluating AI-discovered knowledge/rules in the arts and humanities. From a specific scenario, we present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects. Our goal is to reveal both the possibilities and the challenges in such a process of decoding expressive messages from AI sources. We treat this as a first step towards 1) better design of AI representations that are human interpretable and 2) a general methodology to evaluate interpretability of AI-discovered knowledge representations.