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Title: DescribeML: A dataset description tool for machine learning
Author: Giner Miguelez, Joan  
Gómez, Abel  
Cabot, Jordi  
Citation: Giner-Miguelez, J. [Joan], Gómez, A. [Abel] & Cabot, J. [Jordi]. (2024). DescribeML: A dataset description tool for machine learning. Science of Computer Programming, 231, 103030. doi: 10.1016/j.scico.2023.103030
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
machine learning
model-driven engineering
domain-specific languages
Document type: info:eu-repo/semantics/article
Version: info:eu-repo/semantics/publishedVersion
Issue Date: 2-Jan-2024
Publication license:  
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