Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Evaluation of Interpretability: The Case of AI-Generated Music
  Knowledge   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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The categories of this paper are: alg-geom
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
3
The categories of this paper are: physics
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys