When trying to delve deeper into machine learning, you often come across the metaphor of a “black box”. At the same time, there are a lot of conjecture and confusing definitions about this concept, so it’s quite hard to figure out what’s really going on.
So, let's break down what does a black box in machine learning mean, what situations black box models work best for, and what issues may be connected with this concept.
The “black box” concept was first mentioned during the development of cybernetics and behaviorism and referred to a system that could be observed from the point of view of its inputs and outputs, without knowing anything about its internal mechanisms.
To better understand this concept, let’s take a look at the Skinner Box experiment, a tool meant to study learned behavior.
You get a box with input elements like switches and buttons, as well as output elements in the form of lights being turned on or off. As soon as you feed the set of inputs into the box, you’ll be able to see the corresponding outputs without seeing anything inside the box. Even if you could see how everything inside the box works, you’d have a hard time understanding why each component is placed where it is, and why it does what it does.
To understand this peculiar relationship, we need to focus on the fact that the components follow strict rules that dictate their individual behavior, while the general behavior of the whole system arises from their interactions.
Getting back to black boxes in artificial intelligence (AI) or machine learning (ML) systems, let’s consider why the black box is relevant here:
Now that you know a little bit about the concept of a black box, let’s now find out in what situations you can justify using black box machine learning.
Despite a lot of benefits AI and ML bring to everyday life, it’s still going through some growing pains. Aside from the problem of bias, artificial intelligence also faces the black box problem illustrated in the following:
Since the issues that occur within artificial intelligence or machine learning models may cause harm when the algorithms are applied to the critically important tasks, they require an immediate solution. Here’s how one can resolve the black box problems:
The black box concept is a vital topic in the field of artificial intelligence in general, and in machine learning systems in particular. It’s especially widespread in neural networks and often brings problems to their users because of their complex and unpredictable nature. Consequently, people don’t trust these models.
To prevent issues from occurring within critical tasks, it’s important to resolve them in advance by making the ML system more transparent, using outside tools that monitor how it works. These and other methods will help to make your models predictable and let people understand why the system works the way it does.
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