  Artificial Intelligence systems, which benefit from the availability of
large-scale datasets and increasing computational power, have become effective
solutions to various critical tasks, such as natural language understanding,
speech recognition, and image processing. The advancement of these AI systems
is inseparable from open-source software (OSS). This paper presents an
empirical study that investigates the issues in the repositories of open-source
AI repositories to assist developers in understanding problems during the
process of employing AI systems. We collect 576 repositories from the
PapersWithCode platform. Among these repositories, we find 24,953 issues by
utilizing GitHub REST APIs. Our empirical study includes three phases. First,
we manually analyze these issues to categorize the problems that developers are
likely to encounter in open-source AI repositories. Specifically, we provide a
taxonomy of 13 categories related to AI systems. The two most common issues are
runtime errors (23.18%) and unclear instructions (19.53%). Second, we see that
67.5% of issues are closed. We also find that half of these issues resolve
within four days. Moreover, issue management features, e.g., label and assign,
are not widely adopted in open-source AI repositories. In particular, only
7.81% and 5.9% of repositories label issues and assign these issues to
assignees, respectively. Finally, we empirically show that employing GitHub
issue management features and writing issues with detailed descriptions
facilitate the resolution of issues. Based on our findings, we make
recommendations for developers to help better manage the issues of open-source
AI repositories and improve their quality.
