@inproceedings{9bf0d250028c40728746119bf39583dc,
title = "Towards conceptual models for machine learning computations",
abstract = "We make the case for conceptual models that give the human designer full visibility and control over key aspects of ML applications, including input data preparation, training and inference of the ML models. Our models aim to: (i) achieve better documentation of ML analytics (ii) provide a foundation for a chain of trust in the ML analytics outcome (iii) provide a lever to enforce ethical and legal constraints within the ML pipeline. Representational models can dramatically increase reusability of large-scale ML analytics, while decreasing their roll-out time and cost. Also, they will support novel solutions to time-honored issues of analytics like non-uniform data veracity, privacy and latency profiles.",
keywords = "Artificial Intelligence, Big data analytics, Machine learning",
author = "Ernesto Damiani and Fulvio Frati",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 37th International Conference on Conceptual Modeling, ER 2018 ; Conference date: 22-10-2018 Through 25-10-2018",
year = "2018",
doi = "10.1007/978-3-030-00847-5_1",
language = "British English",
isbn = "9783030008468",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "3--9",
editor = "Zhanhuai Li and Trujillo, {Juan C.} and Xiaoyong Du and Lee, {Mong Li} and Davis, {Karen C.} and Ling, {Tok Wang} and Guoliang Li",
booktitle = "Conceptual Modeling - 37th International Conference, ER 2018, Proceedings",
address = "Germany",
}