What is the black box in machine learning?
Although programmers often have some understanding of what their machine learning algorithms are doing, sometimes they don’t. When they don’t know what’s happening inside the program, even though the AI is getting the right answers, this is generally referred to as a “black box” or a “black box problem.”
The black box is exactly what it sounds like. It’s a missing piece of the puzzle. For programmers, it’s as if the AI tool is going into a room with black windows and figuring out solutions on its own. This isn’t what’s happening; AI tools may be smart, but they’re not sentient. Even so, the program is clearly doing something unexplainable or untraceable due to its complex nature.
It’s important to understand not just that the program can do a job but how it’s getting its answers. If a machine learning algorithm involves a black box, you can’t really be sure it’s always going to give you the right results. It might be correlating the wrong data, resulting in answers that are right most of the time for the wrong reasons.
For example, an AI tool trained to read medical imaging might understand what it’s seeing is a broken leg, or it might just figure out that the data it has is from a machine that generally detects broken legs. So when something vaguely leg-shaped appears, it might draw the conclusion that the leg was broken by correlating the machine and the general shape of the image rather than by identifying an actual break.