AI Black Box: A Demystified Guide

Understanding AI black box that refers to AI systems with core workings that are unseen to the user

Some people associate the phrase "black box" with the recording mechanisms used in aircraft that are useful for postmortem examinations in the event of the unthinkable. Others associate it with tiny, sparsely furnished theatres.

But the phrase "black box" is also significant in artificial intelligence. AI "black boxes" are systems that have unobservable internal operations. You can provide input to them and receive output, but you cannot look at the system's code or the reasoning that led to the output.

The most common branch of artificial intelligence is machine learning. It is the foundation of ChatGPT and DALL-E 2, two generative AI systems. Machine learning consists of a model, training data, and a method or group of algorithms. An algorithm is a collection of steps. In machine learning, an algorithm is trained on a sizable collection of examples, or "training data," and then learns to recognize patterns. A machine-learning model is produced when a machine-learning algorithm has been trained. Humans employ the model.

A machine-learning algorithm, for instance, might be created to find patterns in photos, and the training data might be pictures of dogs. A dog spotter machine learning model would be created as a result. It would take an image as input and return information on whether and where a collection of pixels in the image indicate a dog.

A machine-learning system can have any one of its three components hidden or in a "black box." The algorithm is widely known, as is frequently the case, making using a black box less effective. Thus, AI developers often enclose the model in a black box to safeguard their intellectual property. Another strategy software developers employ is hiding the data used to train the model or placing the training data in a "black box."

Glass boxes are occasionally used to describe the opposite of a black box. An AI glass box is a system whose training data, models, and algorithms are all publicly accessible. However, some academics refer to certain of even these as "black boxes."

This is because deep learning algorithms, in particular, still need to be better understood by experts. Researchers in explainable AI strive to create algorithms that, while not necessarily "glass boxes," are more accessible for people to understand.

Importance of AI Black Box

Black box machine learning techniques and models should generally be avoided. Let's say a machine-learning algorithm has identified a health issue. Would you like a glass box or a black box model? What about the doctor who issued your treatment plan? She could be interested in learning how the model made its choice.

What happens if a machine-learning model used to verify your eligibility for a bank loan for your business rejects you? Do you want to discover the reason? If you did, you may more successfully challenge the ruling or alter your circumstances to improve your loan prospects.

Black boxes have significant effects on software system security as well. Many people in the computing industry believed for many years that placing software within a black box would prevent hackers from looking at it, making it secure. The ability of hackers to reverse-engineer software or create a copy by carefully studying how a piece of software functions and finding weaknesses to exploit has disproved this presumption.

If the software is in a glass box, software testers and well-meaning hackers can examine it and alert the authors of any vulnerabilities, reducing the likelihood of intrusions.