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The term "black box" is used because the actual program being executed is not examined. In computing in general, a black box program is one where the user cannot see the inner workings (perhaps because it is a closed source program) or one which has no side effects and the function of which need not be examined, a routine suitable for re-use.
Machine learning (ML) algorithms used in AI can be categorized as white-box or black-box. [13] White-box models provide results that are understandable to experts in the domain. Black-box models, on the other hand, are extremely hard to explain and may not be understood even by domain experts. [14]
The social constructivist conception of black boxing doesn't delineate the physical components hidden inside an apparent whole; rather, what is black-boxed are associations, various actors from which the box is composed. Opening the hood of an electric car, for example, reveals only mechanical components.
Example of a black box model where a certain input produces a certain output. Specific knowledge of the application's code, internal structure and programming knowledge in general is not required. [3] The tester is aware of what the software is supposed to do but is not aware of how it does it.
Grey box modeling is also known as semi-physical modeling. [8] black box model: No prior model is available. Most system identification algorithms are of this type. In the context of nonlinear system identification Jin et al. [9] describe grey-box modeling by assuming a model structure a priori and then estimating the model parameters ...
Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.
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As a result, AI designers could deploy the system by accident, believing it to be more aligned than it is. To detect such deception, researchers aim to create techniques and tools to inspect AI models and to understand the inner workings of black-box models such as neural networks.