XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process
Open accessAbstract
Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in regard to its practical implementation in various application domains. To combat the lack of understanding of AI-based systems, Explainable AI (XAI) aims to make black-box AI models more transparent and comprehensible for humans. Fortunately, plenty of XAI methods have been introduced to tackle the explainability problem from different perspectives. However, due to the vast search space, it is challenging for ML practitioners and data scientists to start with the development of XAI software and to optimally select the most suitable XAI methods. To tackle this challenge, we introduce XAIR, a novel systematic metareview of the most promising XAI methods and tools. XAIR differentiates itself from existing reviews by aligning its results to the five steps of the software development process, including requirement analysis, design, implementation, evaluation, and deployment. Through this mapping, we aim to create a better understanding of the individual steps of developing XAI software and to foster the creation of real-world AI applications that incorporate explainability. Finally, we conclude with highlighting new directions for future research.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Partial dependence plots result in a significantly biased computed feature effect, while ALEs provide a faster and unbiased substitute for visualisation.
“While partial dependence plots result in a significantly biased computed feature effect, ALEs provide a faster and unbiased substitute for visualisation [138,139].”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [139]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Design Science in Information Systems Research1
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004) · MIS Quarterly · doi:10.2307/25148625 · Cited by 10,707 · see citing works
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Explainable AI: a review of machine learning interpretability methods
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works
Anchors: High-Precision Model-Agnostic Explanations
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162 · see citing works
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131 · see citing works
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477 · see citing works
A Guide to Conducting a Standalone Systematic Literature Review
Okoli, C. (2015) · Communications of the Association for Information Systems · doi:10.17705/1cais.03743 · Cited by 1,413 · see citing works
Explainable AI (XAI): Core Ideas, Techniques, and Solutions
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., et al. (2023) · ACM Computing Surveys · doi:10.1145/3561048 · Cited by 1,386 · see citing works
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040 · see citing works
Quantifying Attention Flow in Transformers
Abnar, S., & Zuidema, W. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.385 · Cited by 995 · see citing works
Transformer Interpretability Beyond Attention Visualization
Chefer, H., Gur, S., & Wolf, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00084 · Cited by 903 · see citing works
Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854 · see citing works
Explainable artificial intelligence: an analytical review
Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1424 · Cited by 843 · see citing works
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598 · see citing works
FactSheets: Increasing trust in AI services through supplier's declarations of conformity
Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942288 · Cited by 434 · see citing works
Responsible AI for Digital Health: a Synthesis and a Research Agenda
Trocin, C., Mikalef, P., Papamitsiou, Z., & Conboy, K. (2023) · Information Systems Frontiers · doi:10.1007/s10796-021-10146-4 · Cited by 205 · see citing works
Explainable Artificial Intelligence for Tabular Data: A Survey
Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135 · see citing works
XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
Lopes, P., Silva, E., Braga, C., Oliveira, T., & Rosado, L. (2022) · Applied Sciences · doi:10.3390/app12199423 · Cited by 112 · see citing works
Explainable Sentiment Analysis with Applications in Medicine
Zucco, C., Liang, H., Fatta, G. D., & Cannataro, M. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621359 · Cited by 59 · see citing works
Yepmo, V., Smits, G., & Pivert, O. (2022) · Data & Knowledge Engineering · doi:10.1016/j.datak.2021.101946 · Cited by 52 · see citing works
Explainable artificial intelligence, lawyer's perspective
Górski, Ł., & Ramakrishna, S. (2021) · Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law · doi:10.1145/3462757.3466145 · Cited by 40 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,062 · see citing works
“While partial dependence plots result in a significantly biased computed feature effect, ALEs provide a faster and unbiased substitute for visualisation [138, 139].”
BackgroundA Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works
Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness
chander, B., John, C., Warrier, L., & Gopalakrishnan, K. (2025) · ACM Computing Surveys · doi:10.1145/3675392 · Cited by 108 · see citing works
Mirzaei, S., Mao, H., Al-Nima, R. R. O., & Woo, W. L. (2023) · Information · doi:10.3390/info15010004 · Cited by 25 · see citing works
- Authors
- Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Software (Germany) (DE)
- Corresponding author
- Clement, Tobias
- Venue
- Machine Learning and Knowledge Extraction
- Publisher
- MDPI AG
- Published
- 2023-01-11
- Volume
- 5
- Issue
- 1
- Pages
- 78-108
- ISSN
- 2504-4990 (ISSN-L)
- OpenAlex
- W4315701371
- Cited by
- 131
- References
- 29
- Citations by year
- 2026: 30 · 2025: 41 · 2024: 44 · 2023: 16
- FWCI
- 12.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Machine Learning and Knowledge Extraction (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OPUS FAU (Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV), on behalf of the Universitätsbibliothek Erlangen-Nürnberg) (submittedVersion, cc-by)
- Funders
- Federal Ministry of Education and Research (02L19C155, 01IS21021A, 01IS17045)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- explainable AI, software development process, requirements analysis, XAI evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
