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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View
Open accessAbstract
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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“The EU is currently working on the Artificial Intelligence Act [17] which makes a distinction between non-high-risk and high-risk AI systems.”
From Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare (2023) · cites this paper as [17]
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“In addition, this move has sparked a general need to provide more transparency of the entire process from data collection to outcome generation (Loyola-Gonzalez, 2019).”
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Metadata
- Authors
- Loyola-Gonzalez, O.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 154096-154113
- ISSN
- 2169-3536 (ISSN-L)
- 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
- gold
- doi.org
- 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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