Towards Guidelines for Assessing Qualities of Machine Learning Systems
| Autor: | Kyoko Ohashi, Julien Siebert, Jens Heidrich, Isao Namba, Koji Nakamichi, Lisa Joeckel, Rieko Yamamoto, Mikio Aoyama |
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| Rok vydání: | 2020 |
| Předmět: |
Training set
Computer science business.industry 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Software quality Trustworthiness 020204 information systems 0202 electrical engineering electronic engineering information engineering Software system Artificial intelligence business Completeness (statistics) computer |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783030587925 QUATIC |
| DOI: | 10.1007/978-3-030-58793-2_2 |
| Popis: | Nowadays, systems containing components based on machine learning (ML) methods are becoming more widespread. In order to ensure the intended behavior of a software system, there are standards that define necessary quality aspects of the system and its components (such as ISO/IEC 25010). Due to the different nature of ML, we have to adjust quality aspects or add additional ones (such as trustworthiness) and be very precise about which aspect is really relevant for which object of interest (such as completeness of training data), and how to objectively assess adherence to quality requirements. In this article, we present the construction of a quality model (i.e., evaluation objects, quality aspects, and metrics) for an ML system based on an industrial use case. This quality model enables practitioners to specify and assess quality requirements for such kinds of ML systems objectively. In the future, we want to learn how the term quality differs between different types of ML systems and come up with general guidelines for specifying and assessing qualities of ML systems. |
| Databáze: | OpenAIRE |
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