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

• URL: http://arxiv.org/abs/2004.06894v1
• Date: Wed, 15 Apr 2020 06:03:34 GMT
• Title: Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge
• Authors: Haizi Yu, Heinrich Taube, James A. Evans, Lav R. Varshney
• Abstract summary: We focus on evaluating AI-discovered knowledge/rules in the arts and humanities. 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.
• Score: 19.508678969335882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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