When AI Must Explain: Making Smart Systems You Can Trust
Deep learning runs in hospitals, in cars, and in places that touch our lives every day, yet these systems often act like closed boxes that nobody understands.
This cause worry for patients, drivers, and families.
Explainable AI tries to open that box by producing clear, human-ready explanations about why a choice was made.
The goal is simple: more trust, more safety, and fairer outcomes.
Researchers are building tools that show which parts of an image or data led to a decision, and that helps doctors, engineers, and judges to check results.
There are still big problems though, like how to test explanations, which methods really work, and when an explanation is useful or just noise.
Scientists grouped methods, looked back at how ideas grew, and compared several ways to explain image decisions; the results were mixed, and more work is needed.
Better tests, clearer rules, and smarter design will help machines be both powerful and transparent.
If we get this right, AI can help without leaving us guessing, especially in healthcare and self-driving cars.
Read article comprehensive review in Paperium.net:
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): ASurvey
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