Can Explainable AI Really Help People Understand Technology?
Imagine a computer that tells you why it made a choice, but does that make sense to you? Researchers try to find ways to measure if an explanation actually helps.
They look at whether people feel happy with the answer, if they truly understand how the system works, and if their trust is fair or too high.
Curiosity also matters — sometimes people ask more questions, sometimes they stop.
Tests check if people rely on the system the right amount, and whether the human and the tech work well together.
The goal is simple: make tools that are clear, useful, and safe to use.
This is not about fancy words but about real people getting real help.
The work shows many ways to check explanations, some methods work better than others, and more study is needed.
If machines explain things better, our choices will be smarter, and that matters to everyone.
Read article comprehensive review in Paperium.net:
Metrics for Explainable AI: Challenges and Prospects
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