The explainability of an algorithm
First, let us differentiate between a black box and a white box algorithm:
- A black box algorithm is one whose logic is not interpretable by humans either due to its complexity or due to its logic being represented in a convoluted manner.
- A white box algorithm is one whose logic is visible and understandable to a human.
Explainability in the context of machine learning refers to our capacity to grasp and articulate the reasons behind an algorithm’s specific outputs. In essence, it gauges how comprehensible an algorithm’s inner workings and decision pathways are to human cognition.
Many algorithms, especially within the machine learning sphere, are often termed “black box” due to their opaque nature. For instance, consider neural networks, which we delve into in Chapter 8, Neural Network Algorithms. These algorithms, which underpin many deep learning applications, are quintessential examples...
