  Publicly available datasets are one of the key drivers for commercial AI
software. The use of publicly available datasets is governed by dataset
licenses. These dataset licenses outline the rights one is entitled to on a
given dataset and the obligations that one must fulfil to enjoy such rights
without any license compliance violations. Unlike standardized Open Source
Software (OSS) licenses, existing dataset licenses are defined in an ad-hoc
manner and do not clearly outline the rights and obligations associated with
their usage. Further, a public dataset may be hosted in multiple locations and
created from multiple data sources each of which may have different licenses.
Hence, existing approaches on checking OSS license compliance cannot be used.
In this paper, we propose a new approach to assessing the potential license
compliance violations if a given publicly available dataset were to be used for
building commercial AI software. We conduct a case study with our approach on 6
commonly used publicly available image datasets. Our results show that there
exists potential risks of license violations associated with all of the studied
datasets if they were used for commercial purposes.
