Why is interpretability important in AI?
The 'black box' nature of many advanced AI models presents a significant challenge. For AI to be trustworthy and useful in critical applications, we need to understand *why* it makes certain decisions. My work on interpretable machine learning aims to transform these opaque systems into 'glass boxes,' revealing the underlying reasoning. This is crucial for debugging, ensuring fairness, and building user confidence.
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