Publications

@article{Giner-Miguelez:SCICO:2024,

title = {DescribeML: A dataset description tool for machine learning},

author = {Joan Giner-Miguelez and Abel G\'{o}mez and Jordi Cabot},

doi = {10.1016/j.scico.2023.103030},

issn = {0167-6423},

year  = {2023},

date = {2023-09-12},

urldate = {2024-01-01},

journal = {Science of Computer Programming},

volume = {231},

pages = {103030},

publisher = {Elsevier BV},

abstract = {Datasets are essential for training and evaluating machine learning models. However, they are also the root cause of many undesirable model behaviors, such as biased predictions. To address this issue, the machine learning community is proposing as a best practice the adoption of common guidelines for describing datasets. However, these guidelines are based on natural language descriptions of the dataset, hampering the automatic computation and analysis of such descriptions. To overcome this situation, we present DescribeML, a language engineering tool to precisely describe machine learning datasets in terms of their composition, provenance, and social concerns in a structured format. The tool is implemented as a Visual Studio Code extension.},

keywords = {Datasets, Domain-Specific Languages (DSLs), Fairness, Machine Learning (ML), Model-Driven Engineering (MDE), Software},

pubstate = {published},

tppubtype = {article}

}