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Candidate Solutions for Defining Explainability Requirements of AI Systems

  • Conference paper
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Requirements Engineering: Foundation for Software Quality (REFSQ 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14588))

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Abstract

[Context and Motivation] Many recent studies highlight explainability as an important requirement that supports in building transparent, trustworthy, and responsible AI systems. As a result, there is an increasing number of solutions that researchers have developed to assist in the definition of explainability requirements. [Question] We conducted a literature study to analyze what kind of candidate solutions are proposed for defining the explainability requirements of AI systems. The focus of this literature review is especially on the field of requirements engineering (RE). [Results] The proposed solutions for defining explainability requirements such as approaches, frameworks, and models are comprehensive. They can be used not only for RE activities but also for testing and evaluating the explainability of AI systems. In addition to the comprehensive solutions, we identified 30 practices that support the development of explainable AI systems. The literature study also revealed that most of the proposed solutions have not been evaluated in real projects, and there is a need for empirical studies. [Contribution] For researchers, the study provides an overview of the candidate solutions and describes research gaps. For practitioners, the paper summarizes potential practices that can help them define and evaluate the explainability requirements of AI systems.

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Authors and Affiliations

  1. Department of Computer Science, Aalto University, Espoo, Finland

    Nagadivya Balasubramaniam, Marjo Kauppinen, Hong-Linh Truong & Sari Kujala

Authors
  1. Nagadivya Balasubramaniam
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  2. Marjo Kauppinen
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  3. Hong-Linh Truong
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  4. Sari Kujala
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Corresponding author

Correspondence to Nagadivya Balasubramaniam .

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Editors and Affiliations

  1. Blekinge Institute of Technology, Karlskrona, Sweden

    Daniel Mendez

  2. NOVA University Lisbon, Lisbon, Portugal

    Ana Moreira

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Balasubramaniam, N., Kauppinen, M., Truong, HL., Kujala, S. (2024). Candidate Solutions for Defining Explainability Requirements of AI Systems. In: Mendez, D., Moreira, A. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2024. Lecture Notes in Computer Science, vol 14588. Springer, Cham. https://doi.org/10.1007/978-3-031-57327-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-57327-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57326-2

  • Online ISBN: 978-3-031-57327-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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