• What is Explainable AI?
Explainable AI, short for Explainable artificial intelligence (XAI), often overlapping with Interpretable AI and Explainable Machine Learning. XAI aims to explain what has been done, what is being done, and what will be done next, and to unveil which information these actions are based on
• Why is Explainable AI Important?
In some fields, e.g. healthcare or financial services, it can be especially important to explain why a particular decision has been taken. It therefore follows that we need to understand why an AI has taken those decisions, hence explainable AI.
• What are the challenges and problems around Explainable AI?
Emergence predicts that as complex systems become more complex, they frequently take on properties and behaviours we cannot predict by looking at their constituent parts
Men in Black: International (2019). Very important to be able to justify why something happened! Gets interesting when we're asking AI models to explain themselves or explain other AI models.
Explainability in some contexts, like LLM (ChatGPT and DALLE) isn't straightforward. There are no neat rules to trace through these giant statistical models; and isn't something a human can open up the hood of and trace how a particular answer was arrived at. However, that doesn't mean explainability doesn't exist. In fact, it very much does exist, but in a very different form.
As in any field - it's just a matter of finding the right (explainability) tool for the right job.
A large part of explaining AI is understanding the logic - the logic follows that to teach a machine to do maths requires it to have an understanding of mathematical principles. And yet, most of us learn our times tables by repetition. We learn that 7 x 6 = 42 not by anything other than repeating that 7 x 6 = 42 until such time as we know it off by heart. When, as school children, we put our hand up in class and answered 42 we were not performing a calculation in our heads, but instead recalling our training data.
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