nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Demystifying Applications of Explainable Artificial Intelligence (XAI) in e-Commerce

verfasst von : S. Faizal Mukthar Hussain, R. Karthikeyan, M. A. Jabbar

Erschienen in: Role of Explainable Artificial Intelligence in E-Commerce

Verlag: Springer Nature Switzerland

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The past ten years have witnessed significant advancements in artificial intelligence (AI), which has led to the widespread implementation of algorithms for the purpose of solving a wide range of issues. Nevertheless, in order to achieve this level of success, model complexity has been increased while at the same time black-box AI models, which lack clearness, have been utilized. With respect to the above mentioned demand, a concept known as XAI is presented with the goal of making AI more understandable and, as a result, accelerating the adoption of AI in important areas. Several relative ideas of XAI prime part in the published article, these difficulties and research ideas over Explainable AI are dispersed despite the fact that they have been identified. This chapter will provide an introduction to XAI, which will describe Why Explainable AI is needed, the various types of perspective groups, the three unique parts that make up XAI, and issues in XAI.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Chatbot-XAI—The New Age Artificial Intelligence Communication Tool for E-Commerce

Nächstes Kapitel From Algorithms to Ethics: XAI’s Impact on E-Commerce

Saeed, W., & Omlin, C. (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263, 110273, ISSN 0950–7051. https://doi.org/10.1016/j.knosys.2023.110273.

Goodman, B., & Flaxman, S. (2017). European union regulations on algorithmic decision-making and a ‘“right to explanation.”’ AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741CrossRef

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052CrossRef

Gunning, D. (2016). Broad agency announcement explainable artificial intelligence (XAI). Technical Report.

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. preprint arXiv:1702.08608.

Gade, K., Geyik, S. C., Kenthapadi, K., Mithal, V., & Taly, A. (2020).

Explainable AI in industry: practical challenges and lessons learned: implications tutorial. InProceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ‘20).

Association for Computing Machinery. https://doi.org/10.1145/3351095.3375664.

Ahmad, M. A., Teredesai, A., & Eckert, C. (2018). Interpretable machine learning in healthcare. In 2018 IEEE-International Conference on Healthcare Informatics (ICHI) (pp. 447–447). https://doi.org/10.1109/ICHI.2018.00095.

10.

He, F., Wang, Y., Miao, X., & Sun, X. (2021). Interpretable visual reasoning: A survey. Image and Vision Computing, 112, 104194. ISSN-0262-8856. https://doi.org/10.1016/j.imavis.2021.10419.

11.

Liu, M., Shi, J., Cao, K., Zhu, J., & Liu, S. (2018). Analyzing the training processes of deep generative models. IEEE Transaction on Vision Computer Graphics, 24(1), 77–87. https://doi.org/10.1109/TVCG.2017.2744938CrossRef

12.

van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., & Viergever, M. A. (2022). Explainableartificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 79, 102470. https://doi.org/10.1016/j.media.2022.102470CrossRef

13.

Orekondy, T., Schiele, B., & Fritz, M. (2019). Knockoff nets: Stealing functionality of black-box models. In Conference on Computer Vision and Pattern Recognition.

14.

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., & Tygar, J. D. (2011). Adversarial machine learning. In Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, AISec ’11, Association for Computing Machinery (pp. 43–58). https://doi.org/10.1145/2046684.2046692.

15.

Liu, S., Kailkhura, B., Loveland, D., & Han, Y. (2019). Generative counterfactual introspection for explainable deep learning. In 2019 IEEE Global Conference on Signal and Information Processing, Global SIP (pp. 1–5). https://doi.org/10.1109/GlobalSIP45357.2019.8969491.

16.

LeDell, E., & Poirier, S. (2020). H2O autoML: Scalable automatic machine learning. In 7th ICML Workshop on Automated Machine Learning, AutoML. https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf.

17.

Płońska, A., & Płoński, P. (2021). MLJAR: State-of-the-art automated machine learning framework for tabular data. Version 0.10.3, MLJAR, Łapy, Poland. https://github.com/mljar/mljar-supervised.

18.

Panigutti, C., Perotti, A., & Pedreschi, D. (2020). Doctor XAI: An ontology-based approach to black-box sequential data classification explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, in: FAT* ’20, Association for Computing Machinery (pp. 629–639). https://doi.org/10.1145/3351095.3372855.

19.

Confalonieri, R., Weyde, T., Besold, T. R., & del Prado Martín, F. M. (2021). Using ontologies to enhance human understandability of global posthoc explanations of black-box models. Artificial Intelligence, 296, 103471. https://doi.org/10.1016/j.artint.2021.103471.

20.

Tudorache, T. (2020). Ontology engineering: Current state, challenges, and futuredirections. Semant. Web, 11(1), 125–138.CrossRef

Titel: Demystifying Applications of Explainable Artificial Intelligence (XAI) in e-Commerce
verfasst von: S. Faizal Mukthar Hussain
R. Karthikeyan
M. A. Jabbar
Verlag: Springer Nature Switzerland
Buch: Role of Explainable Artificial Intelligence in E-Commerce
Print ISBN: 978-3-031-55614-2

Electronic ISBN: 978-3-031-55615-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-55615-9_7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner