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Cited by 559Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine LearningMachine Learning and Data Classification

The DOI matches its Crossref record; 177 of the 179 references it cites were found in a registry or an index, and 2 are not checked yet.

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

Open access
Loyola-Gonzalez, Octavio·2019·IEEE Access
DOI 10.1109/access.2019.2949286
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Abstract

Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a state-of-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and black-box, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications.

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Research works printed but not linked: 3

  1. 10.1007/BF00153759 · DOI
  2. pinheiro, 2008, A multicriteria model applied in the diagnosis of Alzheimer’s disease: A Bayesian network, Proc 11th IEEE Int Conf Comput Sci Eng, 15
  3. 2016, Broad Agency Announcement Explainable Artificial Intelligence (XAI)

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Metadata

Authors
Loyola-Gonzalez, O.
ORCID
Loyola-Gonzalez, Octavio
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
7
Pages
154096-154113
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2019.2949286
OpenAlex
W2981836045
Cited by
559
References
171
Citations by year
2026: 64 · 2025: 117 · 2024: 128 · 2023: 92 · 2022: 88 · 2021: 54 · 2020: 14 · 2019: 2
FWCI
18.23
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Greater South Information System
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords
black-box models, explainable machine learning, white-box models, model interpretability, expert opinion, model effectiveness evaluation, security applications, medical applications, image preprocessing

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