How do ML practitioners perceive explainability? an interview study of practices and challenges

Explainable artificial intelligence (XAI) is a field of study that focuses on the development process of AI-based systems while making their decision-making processes understandable and transparent for users. Research already identified explainability as an emerging requirement for AI-based systems...

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Veröffentlicht in:	Empirical software engineering : an international journal Jg. 30; H. 1; S. 18
Hauptverfasser:	Habiba, Umm-e-, Habib, Mohammad Kasra, Bogner, Justus, Fritzsch, Jonas, Wagner, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.02.2025 Springer Nature B.V
Schlagworte:	Artificial intelligence Compilers Computer Science Decision making Explainable artificial intelligence Interpreters Interviews Machine learning Professional ethics Programming Languages Quality management Requirements analysis Software engineering Software Engineering/Programming and Operating Systems Software quality Stakeholders Subject specialists Requirements engineering Interviews Explainable AI
ISSN:	1382-3256, 1573-7616
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Abstract	Explainable artificial intelligence (XAI) is a field of study that focuses on the development process of AI-based systems while making their decision-making processes understandable and transparent for users. Research already identified explainability as an emerging requirement for AI-based systems that use machine learning (ML) techniques. However, there is a notable absence of studies investigating how ML practitioners perceive the concept of explainability, the challenges they encounter, and the potential trade-offs with other quality attributes. In this study, we want to discover how practitioners define explainability for AI-based systems and what challenges they encounter in making them explainable. Furthermore, we explore how explainability interacts with other quality attributes. To this end, we conducted semi-structured interviews with 14 ML practitioners from 11 companies. Our study reveals diverse viewpoints on explainability and applied practices. Results suggest that the importance of explainability lies in enhancing transparency, refining models, and mitigating bias. Methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) are frequently used by ML practitioners to understand how models work, while tailored approaches are typically adopted to meet the specific requirements of stakeholders. Moreover, we have discerned emerging challenges in eight categories. Issues such as effective communication with non-technical stakeholders and the absence of standardized approaches are frequently stated as recurring hurdles. We contextualize these findings in terms of requirements engineering and conclude that industry currently lacks a standardized framework to address arising explainability needs.
AbstractList	Explainable artificial intelligence (XAI) is a field of study that focuses on the development process of AI-based systems while making their decision-making processes understandable and transparent for users. Research already identified explainability as an emerging requirement for AI-based systems that use machine learning (ML) techniques. However, there is a notable absence of studies investigating how ML practitioners perceive the concept of explainability, the challenges they encounter, and the potential trade-offs with other quality attributes. In this study, we want to discover how practitioners define explainability for AI-based systems and what challenges they encounter in making them explainable. Furthermore, we explore how explainability interacts with other quality attributes. To this end, we conducted semi-structured interviews with 14 ML practitioners from 11 companies. Our study reveals diverse viewpoints on explainability and applied practices. Results suggest that the importance of explainability lies in enhancing transparency, refining models, and mitigating bias. Methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) are frequently used by ML practitioners to understand how models work, while tailored approaches are typically adopted to meet the specific requirements of stakeholders. Moreover, we have discerned emerging challenges in eight categories. Issues such as effective communication with non-technical stakeholders and the absence of standardized approaches are frequently stated as recurring hurdles. We contextualize these findings in terms of requirements engineering and conclude that industry currently lacks a standardized framework to address arising explainability needs.
ArticleNumber	18
Author	Habib, Mohammad Kasra Bogner, Justus Habiba, Umm-e- Wagner, Stefan Fritzsch, Jonas
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Cites_doi	10.3389/fcomp.2023.1117848 10.1109/REW53955.2021.00031 10.1109/RE.2019.00046 10.1109/DSAA.2018.00018 10.1109/ISSREW.2019.00035 10.1109/RE48521.2020.00046 10.1016/j.jbi.2020.103655 10.1016/j.dsp.2017.10.011 10.1109/ISR50024.2021.9419498 10.1145/3561048 10.1109/REW53955.2021.00033 10.1007/978-3-030-68796-0_1 10.21203/rs.3.rs-2963888/v1 10.3390/electronics8080832 10.1007/s10664-008-9102-8 10.1109/RE51729.2021.00025 10.1145/3236386.3241340 10.1007/978-1-84800-044-5_2 10.1016/j.artint.2018.07.007 10.1007/s10664-021-10072-8 10.1145/3334480.3383047 10.1088/1741-2552/ab8131 10.1016/j.eswa.2019.113100 10.1109/REW53955.2021.00032 10.1109/REW.2019.00050 10.1145/2783258.2788613 10.1007/s00766-020-00333-1 10.1145/2939672.2939778 10.1145/3411764.3445088 10.1109/REW56159.2022.00038
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SubjectTerms	Artificial intelligence Compilers Computer Science Decision making Explainable artificial intelligence Interpreters Interviews Machine learning Professional ethics Programming Languages Quality management Requirements analysis Software engineering Software Engineering/Programming and Operating Systems Software quality Stakeholders Subject specialists
Title	How do ML practitioners perceive explainability? an interview study of practices and challenges
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