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1. • • To summarize, a whitebox view of a system model shows the internal workings of the system, while a blackbox view shows the system's external behavior without revealing the details of its internal workings.
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   What is the difference between a white-box and a black-box model?

   • The training continues until the accuracy of the output of the reasoner is maximized by updating the representations. An AI model can be (i) white-box or (ii) black-box. A white-box model is explainable by design. Therefore, it does not require additional capabilities to be explainable. A black-box model is not explainable by itself.

   Black-box and White-Box Models towards Explainable AI

   towardsdatascience.com/black-box-and-white-box-models-towards-explainable-ai-172d45bfc512

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   What is a white box machine learning model?

   • A white box machine learning model (White Box) is one that allows humans to easily interpret how it was able to produce its output and draw its conclusions, thereby giving us insight into the algorithm’s inner workings. White boxes are transparent in terms of: Examples of such models include linear trees, decision trees, and regression trees.

   White Box vs. Black Box Algorithms in Machine Learning - ActiveState

   www.activestate.com/blog/white-box-vs-black-box-algorithms-in-machine-learning/

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   Can a black box model be explainable?

   • A white-box model is explainable by design. Therefore, it does not require additional capabilities to be explainable. A black-box model is not explainable by itself. Therefore, to make a black-box model explainable, we have to adopt several techniques to extract explanations from the inner logic or the outputs of the model.

   Black-box and White-Box Models towards Explainable AI

   towardsdatascience.com/black-box-and-white-box-models-towards-explainable-ai-172d45bfc512

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   What is a black box machine learning model?

   • Black box machine learning models (Black Boxes), on the other hand, rank higher on innovation and accuracy, but lower on transparency and interpretability. Black Boxes produce output based on your input data set, but do not – and cannot – clarify how they came to those conclusions.

   White Box vs. Black Box Algorithms in Machine Learning - ActiveState

   www.activestate.com/blog/white-box-vs-black-box-algorithms-in-machine-learning/

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3. towardsdatascience.com › machine-learningBlack-box vs. white-box models. Most machine learning systems ...

   towardsdatascience.com › machine-learning

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   Mar 14, 2019 · They serve primarily as ‘global summary’ of a model. The following steps illustrate how you can build a surrogate model for any black-box model. Train a black-box model. Evaluate the black box model on the dataset. Choose an interpretable surrogate model (typically a linear model or a decision tree).

   • Author: Lars Hulstaert

4. www.toolify.ai › ai-news › understanding-theUnderstanding the Differences Between Black Box and White Box ...

   www.toolify.ai › ai-news › understanding-the

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   In the world of machine learning and artificial intelligence, two terms frequently pop up: black box models and white box models. Each of these models has its own distinctive characteristics, advantages, and applications.

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6. ieeexplore.ieee.org › document › 8882211Black-Box vs. White-Box: Understanding Their Advantages and ...

   ieeexplore.ieee.org › document › 8882211

   Oct 24, 2019 · 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.

   • Author: Octavio Loyola-González

   • Publish Year: 2019

7. www.activestate.com › blog › white-box-vs-black-boxWhite Box vs. Black Box Algorithms in Machine Learning

   www.activestate.com › blog › white-box-vs-black-box

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   Jul 19, 2023 · A white box machine learning model (White Box) is one that allows humans to easily interpret how it was able to produce its output and draw its conclusions, thereby giving us insight into the algorithm’s inner workings. White boxes are transparent in terms of: How they behave. How they process data.

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9. towardsdatascience.com › understanding-a-black-boxUnderstanding a Black-Box. An overview of model ...

   towardsdatascience.com › understanding-a-black-box

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   Aug 12, 2018 · The simplest method one can think of is slight alterations of input data to observe how the underlying black box is reacting. For visual data usage of partially occluded images is the easiest method. For text — the substitution of words, and for numerical/categorical data — alteration of variables. Easy as that!

10. blog.dataiku.com › white-box-vs-black-box-modelsWhite Box vs Black Box Models: Finding the Balance - Dataiku

    blog.dataiku.com › white-box-vs-black-box-models

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    Jan 31, 2020 · White Box vs Black Box Models: Finding the Balance. Data scientists and business leaders building or using machine learning models and AI systems face a serious challenge today — how to balance interpretability and accuracy stemming from the difference between black-box and white-box models.