The term "black box" evokes various images, ranging from vital recording devices in aircraft cockpits to small theatrical performances. However, "black box" is also a significant term in the field of artificial intelligence, where mystery surrounds systems that process information and produce answers without revealing how they arrive at these results. In this intriguing world, the "black box" resembles a complex puzzle: we can know what goes in and comes out, but the inner workings remain hidden.
In artificial intelligence, the black box refers to systems that operate invisibly to the user. They can be fed information and yield results, but the system's code or logic that produced those results cannot be examined. Machine learning is a primary component under the umbrella of artificial intelligence, relied upon in generative AI systems like ChatGPT and DALL-E2. There are three components to machine learning: algorithms or a set of algorithms, training data, and the model.
The algorithm is a set of procedures. In machine learning, the algorithm learns to recognize patterns after being trained on a large set of examples – the training data.
After training a machine learning algorithm, the result is the machine learning model. The model is what people use.
For instance, a machine learning algorithm can be designed to recognize patterns appearing in images, like a set of dog pictures. The resulting machine learning model would be for detecting dogs. Given an input image, it provides an output indicating whether there are pixel clusters representing a dog and where they are.
Any of the three components of the machine learning system can be hidden or put into a "black box." Algorithms are often known to the public, making placing them in a black box less effective. To protect their intellectual property, AI developers frequently place the model in a black box. Another approach taken by software developers is to hide the training data used to train the model; in other words, putting the training data in a black box. The opposite of the black box is sometimes called the "glass box."
An "glass box" artificial intelligence system is one where its algorithms, training data, and model are accessible to everyone for inspection. However, even these systems are sometimes viewed by researchers as black boxes because they don't fully understand how deep learning algorithms, in particular, work. The field of interpretable artificial intelligence aims to develop algorithms that, while not necessarily glass boxes, are more understandable to humans.
Why the "black box" is important in artificial intelligence:
Often, there is a genuine reason to be cautious about black-box algorithms and machine learning models. Imagine an artificial intelligence program analyzing your health and providing a diagnosis. Would you prefer this program to be closed, not understandable in its workings, or transparent and open for you to understand how it arrived at that conclusion? What about the doctor relying on this program to determine your treatment? Surely, you'd want to understand the logic behind the program's decision. If the AI program denies your loan application for your business, wouldn't you like to know the reasons? Knowing them enables you to effectively challenge the decision or improve your position for future loan applications.
Black boxes also have significant impacts on software security. For a long time, many in the computer field believed that hiding and making software unexamined would make it secure. However, reality showed these assumptions to be false because hackers can analyze and replicate software, creating a similar version by studying how it works and discovering vulnerabilities to exploit.
If software is transparent – a "glass box" – software testers and ethical hackers can analyze and alert developers to any weaknesses, reducing the likelihood of electronic attacks.
