How Explainable Is Your System? Towards a Quality Model for Explainability

verfasst von: Hannah Deters, Jakob Droste, Martin Obaidi, Kurt Schneider
Abstract: [Context and motivation] Explainability is a software quality aspect that is gaining relevance in the field of requirements engineering. The complexity of modern software systems is steadily growing. Thus, understanding how these systems function becomes increasingly difficult. At the same time, stakeholders rely on these systems in an expanding number of crucial areas, such as medicine and finance. [Question/problem] While a lot of research focuses on how to make AI algorithms explainable, there is a lack of fundamental research on explainability in requirements engineering. For instance, there has been little research on the elicitation and verification of explainability requirements. [Principal ideas/results] Quality models provide means and measures to specify and evaluate quality requirements. As a solid foundation for our quality model, we first conducted a literature review. Based on the results, we then designed a user-centered quality model for explainability. We identified ten different aspects of explainability and offer criteria and metrics to measure them. [Contribution] Our quality model provides metrics that enable software engineers to check whether specified explainability requirements have been met. By identifying different aspects of explainability, we offer a view from different angles that consider different goals of explanations. Thus, we provide a foundation that will improve the management and verification of explainability requirements.
Organisationseinheit(en): Fachgebiet Software Engineering
Typ: Beitrag in Buch/Sammelwerk
Seiten: 3-19
Anzahl der Seiten: 17
Publikationsdatum: 30.03.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Theoretische Informatik, Informatik (insg.)
Elektronische Version(en): https://doi.org/10.1007/978-3-031-57327-9_1 (Zugang: Geschlossen)

BibTeX

@inbook{92775e93c37241ba8aae6b5c5b9d8d9d,
title = "How Explainable Is Your System? Towards a Quality Model for Explainability",
abstract = "[Context and motivation] Explainability is a software quality aspect that is gaining relevance in the field of requirements engineering. The complexity of modern software systems is steadily growing. Thus, understanding how these systems function becomes increasingly difficult. At the same time, stakeholders rely on these systems in an expanding number of crucial areas, such as medicine and finance. [Question/problem] While a lot of research focuses on how to make AI algorithms explainable, there is a lack of fundamental research on explainability in requirements engineering. For instance, there has been little research on the elicitation and verification of explainability requirements. [Principal ideas/results] Quality models provide means and measures to specify and evaluate quality requirements. As a solid foundation for our quality model, we first conducted a literature review. Based on the results, we then designed a user-centered quality model for explainability. We identified ten different aspects of explainability and offer criteria and metrics to measure them. [Contribution] Our quality model provides metrics that enable software engineers to check whether specified explainability requirements have been met. By identifying different aspects of explainability, we offer a view from different angles that consider different goals of explanations. Thus, we provide a foundation that will improve the management and verification of explainability requirements.",
keywords = "Explainability, Literature Studies, Metrics, Quality Models, Requirements Engineering",
author = "Hannah Deters and Jakob Droste and Martin Obaidi and Kurt Schneider",
note = "Funding Information: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No.: 470146331, project softXplain (2022-2025).",
year = "2024",
month = mar,
day = "30",
doi = "10.1007/978-3-031-57327-9_1",
language = "English",
isbn = "9783031573262",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature Switzerland AG",
pages = "3--19",
editor = "Daniel Mendez and Daniel Mendez and Ana Moreira and Ana Moreira",
booktitle = "Requirements Engineering",
address = "Switzerland",
}