If you're investing in artificial intelligence or integrating it into your regular work, it's important to understand the terminology associated with it. For example, there are different types of artificial intelligence, including xAI. If you've been asking yourself, "What is xAI?" you've come to the right place.
What is xAI?
What is xAI?
The term xAI, short for explainable artificial intelligence, is a type of AI model that theoretically has behavior and results that can be fully understood and explained. There are many other types of AI that exist right now that are not easy to understand or explain how they took their inputs and created presumably accurate outputs from them.
This is the difference between a "white-box" artificial intelligence like xAI and a "black-box" artificial intelligence. In a white-box artificial intelligence, the process is transparent and makes it easy for the model to explain how it's working and for the humans who use it to understand what it's doing.
What is a black box?
What is the black box in artificial intelligence?
It might be really surprising to learn, but a lot of AI machine learning models don't really do work that their programmers or operators fully understand. Although they seem to be getting the right answers, they don't really explain how they work or give any hints about what's going on under the hood. To put it in more human terms, they can't tell you what or how they're thinking.
Because AI is such complicated programming, it's easy for the program to take on a metaphorical life of its own. It's still limited to the functions programmed within it, but sometimes the software comes up with accurate answers without using an accurate process. When this thinking is obscured, it's said to be working in a "black box," also known as "the black-box problem."
White vs. black box
Why does it matter if machine learning is white-box or black-box?
An AI's ability to generate an accurate answer is the entire purpose for having it. If the answer's process is hidden from observers, and no one is really sure what it's doing, it's difficult to trust it for precision work. That's why white-box AIs like xAI are so important. With a black-box AI, you get an answer, but if you need to know for sure that the machine understands its job, you simply can't. With a white-box AI, the AI can tell you exactly what it's doing, why it's doing it, and how reliable it is.
For example, an AI in healthcare that's meant to read chest X-rays may be able to identify a case of COVID-19 pneumonia a high percentage of the time, but if there's a black box involved, you can't say how it knows that. In one case, it was observed that the AI wasn't identifying pneumonia at all but observing that the probability that a particular machine's usage would predict COVID-19 pneumonia was high. Although it came up with the right answer often enough, it did it the wrong way, making the results of that AI system sketchy at best.
If this had been done with a white-box AI like xAI, researchers would have known immediately that the AI was utilizing the data incorrectly and would have been able to retrain it right away. Since this happened in a black-box AI, it took some time to work the bugs out, and if it had been fully implemented, it would have given doctors an AI that was essentially guessing all the time and worthless.
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Why is xAI different?
How does xAI differ from other types of AI?
An xAI is different from other types of AI in that it can tell the user or programmer what it's doing and why it's doing it. It understands its own motivations and thought processes. Its benefits can be broken down into three main components.
- Prediction accuracy: Because we know how xAI is working, we can easily run tests using the training data sets to see how accurate it is. We can do this over and over, being able to see every step along the way until the prediction accuracy is within acceptable parameters.
- Traceability: Since xAI knows what and how it's thinking about problems, unlike a black-box AI, it can tell us exactly what's happening every step of the way. This is great for troubleshooting and for walking back through an AI decision process to see exactly how it arrived at an answer that might be surprising or inaccurate.
- Decision understanding: Unlike a black-box AI, xAI is very easy to understand for both programmers and users. It can show its team how it did the work, and it can show the results of the work, increasing the human trust factor. When we understand why it's doing what it's doing, we're far more likely to trust that it did the job correctly.
