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Explainable AI (XAI) techniques aim to make machine learning models:
- Faster
- More accurate
- Interpretable and transparent
- Smaller in size
Answer: Interpretable and transparent
XAI addresses 'black box' problem in complex models (deep learning) by providing human-understandable explanations for decisions. Critical for high-stakes domains: healthcare diagnostics, credit scoring, judicial support. [[NITI Aayog Principles for Responsible AI]]