AI Black Box: What It Is and How It Works
In Brief
AI black boxes are systems that operate without user knowledge, such as machine learning, which is composed of an algorithm, training data and a model.
Black boxes are important for software security, as they can be used to reverse engineer software and discover flaws to exploit, and can be used by software testers and hackers to find weaknesses.
For many, the term “black box” refers to recording devices in planes that are valuable for postmortem examinations if the unthinkable happens. For others, it is a small, minimally furnished theatre. However, black boxes are also vital to artificial intelligence.
AI black boxes are systems that operate without user knowledge. You can feed them input and get output, but you cannot examine the system’s code or the logic used to generate the output.
Machine learning is the dominant type of artificial intelligence. It comprises an algorithm or a set of algorithms, training data, and a model.
- An algorithm is a sequence of procedures. After being trained, an algorithm is able to recognize known patterns.
- The training data is the data set used for training the AI model.
- A machine-learning algorithm is, in essence, a procedure that is designed to learn from a large number of examples and produce a machine-learning model. A machine-learning model is what people use once it has been created.
An image-recognition algorithm could be programmed to discover image trends, and training data could represent photos of dogs. You would feed it an image as input and get it as output whether and where in the image a set of pixels appears to represent a dog.
Since machine learning algorithms are publicly known, hiding black boxes is less effective. Since AI engineers frequently conceal their intellectual property in black boxes, they usually put the model in one. Another way software developers conceal data is by obscuring the data that are used to train the model – in other words, putting the training data in a black box.
It is difficult to understand how black box algorithms operate, but that is not quite black and white.
A glass box refers to a system whose algorithms, training data, and models are publicly accessible, while a black box refers to a system whose algorithms, training data, and models are concealed. The term black box is often used when researchers describe even these aspects of an AI system as black.
There is a dearth of knowledge about how machine learning algorithms, particularly deep learning algorithms, function. Researchers are developing algorithms that, while not necessarily glass boxes, can be better understood by humans.
Why Are AI Black Boxes Important?
It is not always a good idea to trust black-box machine learning algorithms and models. What if a machine learning model that determines whether you qualify for a business loan from a bank turns you down? You would like to know so you can better appeal the decision or change your situation to increase your chances of getting a loan the next time.
Keeping software in a black box has been thought to prevent hackers from examining it and, therefore, render it secure. However, hackers can reverse engineer software – that is, study how a piece of software works closely – and discover flaws to exploit. Black boxes also have important implications for software system security.
It’s possible for software testers and well-intentioned hackers to look inside a glass box used to test software to find weaknesses, thus cutting down on cyberattacks.
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About The Author
Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet.
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Damir is the team leader, product manager, and editor at Metaverse Post, covering topics such as AI/ML, AGI, LLMs, Metaverse, and Web3-related fields. His articles attract a massive audience of over a million users every month. He appears to be an expert with 10 years of experience in SEO and digital marketing. Damir has been mentioned in Mashable, Wired, Cointelegraph, The New Yorker, Inside.com, Entrepreneur, BeInCrypto, and other publications. He travels between the UAE, Turkey, Russia, and the CIS as a digital nomad. Damir earned a bachelor's degree in physics, which he believes has given him the critical thinking skills needed to be successful in the ever-changing landscape of the internet.
