How Explainable Is Your System? Towards a Quality Model for Explainability | SpringerLink
Skip to main content
Springer Nature Link
Log in

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

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

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

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 7549
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 9437
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Supplementary material. https://doi.org/10.5281/zenodo.10640067

  2. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  3. AL-Badareen, A.B., Selamat, M.H., Jabar, M.A., Din, J., Turaev, S.: Software quality models: a comparative study. In: Mohamad Zain, J., Wan Mohd, W.M., El-Qawasmeh, E. (eds.) ICSECS 2011. CCIS, vol. 179, pp. 46–55. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22170-5_4

    Chapter  Google Scholar 

  4. Al-Qutaish, R.E.: Quality models in software engineering literature: an analytical and comparative study. J. Am. Sci. 6(3), 166–175 (2010)

    Google Scholar 

  5. Brunotte, W., Specht, A., Chazette, L., Schneider, K.: Privacy explanations - a means to end-user trust. J. Syst. Softw. 195, 111545 (2023). https://doi.org/10.1016/j.jss.2022.111545. https://www.sciencedirect.com/science/article/pii/S0164121222002217

  6. Caro-Martínez, M., Jiménez-Díaz, G., Recio-García, J.A.: Conceptual modeling of explainable recommender systems: an ontological formalization to guide their design and development. J. Artif. Intell. Res. 71, 557–589 (2021). https://doi.org/10.1613/jair.1.12789

  7. Chazette, L., Brunotte, W., Speith, T.: Exploring explainability: a definition, a model, and a knowledge catalogue. In: 2021 IEEE 29th International Requirements Engineering Conference (RE), pp. 197–208. IEEE (2021)

    Google Scholar 

  8. Chazette, L., Klös, V., Herzog, F., Schneider, K.: Requirements on explanations: a quality framework for explainability. In: 2022 IEEE 30th International Requirements Engineering Conference (RE), pp. 140–152. IEEE (2022)

    Google Scholar 

  9. Chazette, L., Schneider, K.: Explainability as a non-functional requirement: challenges and recommendations. Requirements Eng. 25(4), 493–514 (2020)

    Article  Google Scholar 

  10. Das, A., Rad, P.: Opportunities and challenges in explainable artificial intelligence (XAI): a survey. CoRR abs/2006.11371 (2020). https://arxiv.org/abs/2006.11371

  11. Deters, H., Droste, J., Schneider, K.: A means to what end? Evaluating the explainability of software systems using goal-oriented heuristics. In: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, pp. 329–338 (2023)

    Google Scholar 

  12. Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: a survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215. IEEE (2018)

    Google Scholar 

  13. Droste, J., Deters, H., Puglisi, J., Klünder, J.: Designing end-user personas for explainability requirements using mixed methods research. In: 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), pp. 129–135. IEEE (2023)

    Google Scholar 

  14. Fox, M., Long, D., Magazzeni, D.: Explainable planning. arXiv preprint arXiv:1709.10256 (2017)

  15. Hoffman, R.R., Klein, G., Mueller, S.T.: Explaining explanation for “explainable AI”. Proc. Hum. Fact. Ergon. Soc. Annu. Meet. 62(1), 197–201 (2018). https://doi.org/10.1177/1541931218621047

  16. Honglei, T., Wei, S., Yanan, Z.: The research on software metrics and software complexity metrics. In: 2009 International Forum on Computer Science-Technology and Applications, vol. 1, pp. 131–136. IEEE (2009)

    Google Scholar 

  17. Institute of Electrical and Electronics Engineers: IEEE standard for a software quality metrics methodology. IEEE Std 1061-1992, pp. 1–96 (1993). https://doi.org/10.1109/IEEESTD.1993.115124

  18. Kitchenham, B.: Procedures for performing systematic reviews. Keele, UK, Keele University 33(2004), 1–26 (2004)

    Google Scholar 

  19. Köhl, M.A., Baum, K., Langer, M., Oster, D., Speith, T., Bohlender, D.: Explainability as a non-functional requirement. In: 2019 IEEE 27th International Requirements Engineering Conference (RE), pp. 363–368. IEEE (2019)

    Google Scholar 

  20. Kuhrmann, M., Tell, P., Klünder, J., Hebig, R., Licorish, S., MacDonell, S.: HELENA stage 2 results. ResearchGate (2018)

    Google Scholar 

  21. Langer, M., et al.: What do we want from explainable artificial intelligence (XAI)? - A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research. Artif. Intell. 296 (2021). https://doi.org/10.1016/j.artint.2021.103473. https://www.sciencedirect.com/science/article/pii/S0004370221000242

  22. McLean, J.: Security models. Encycl. Softw. Eng. 2, 1136–1145 (1994)

    Google Scholar 

  23. Mladenova, T.: Software quality metrics-research, analysis and recommendation. In: 2020 International Conference Automatics and Informatics (ICAI), pp. 1–5. IEEE (2020)

    Google Scholar 

  24. Nunes, I., Jannach, D.: A systematic review and taxonomy of explanations in decision support and recommender systems. User Model. User-Adap. Inter. 27(3), 393–444 (2017)

    Article  Google Scholar 

  25. Samadhiya, D., Wang, S.H., Chen, D.: Quality models: role and value in software engineering. In: 2010 2nd International Conference on Software Technology and Engineering, vol. 1, pp. V1–320. IEEE (2010)

    Google Scholar 

  26. Schneider, J., Handali, J.: Personalized explanation in machine learning. CoRR abs/1901.00770 (2019)

    Google Scholar 

  27. Sokol, K., Flach, P.: Explainability fact sheets: a framework for systematic assessment of explainable approaches. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* 2020, pp. 56–67. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3351095.3372870

  28. Sokol, K., Flach, P.: One explanation does not fit all: the promise of interactive explanations for machine learning transparency. KI-Künstliche Intelligenz 34(2), 235–250 (2020)

    Article  Google Scholar 

  29. Speith, T., Langer, M.: A new perspective on evaluation methods for explainable artificial intelligence (XAI). In: 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), pp. 325–331. IEEE (2023)

    Google Scholar 

  30. Tintarev, N., Masthoff, J.: Effective explanations of recommendations: user-centered design. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 153–156 (2007)

    Google Scholar 

  31. Tintarev, N., Masthoff, J.: Designing and evaluating explanations for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 479–510. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_15

    Chapter  Google Scholar 

  32. Vilone, G., Longo, L.: Notions of explainability and evaluation approaches for explainable artificial intelligence. Inf. Fusion 76, 89–106 (2021). https://doi.org/10.1016/j.inffus.2021.05.009. https://www.sciencedirect.com/science/article/pii/S1566253521001093

  33. Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in Software Engineering. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29044-2

Download references

Acknowledgments

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No.: 470146331, project softXplain (2022-2025).

Author information

Authors and Affiliations

  1. Software Engineering Group, Leibniz University Hannover, Hannover, Germany

    Hannah Deters, Jakob Droste, Martin Obaidi & Kurt Schneider

Authors
  1. Hannah Deters
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jakob Droste
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Obaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kurt Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hannah Deters .

Editor information

Editors and Affiliations

  1. Blekinge Institute of Technology, Karlskrona, Sweden

    Daniel Mendez

  2. NOVA University Lisbon, Lisbon, Portugal

    Ana Moreira

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deters, H., Droste, J., Obaidi, M., Schneider, K. (2024). How Explainable Is Your System? Towards a Quality Model for Explainability. 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_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-57327-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57326-2

  • Online ISBN: 978-3-031-57327-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics