Interpretability vs. Explainability - How do they compare?

Artificial intelligence (AI) is increasingly integrated into our daily lives, and trust in AI models is crucial. Interpretability refers to translating an AI model's inner workings into simple explanations that boost human understanding, while explainability focuses on providing higher-level insight into the model's decision-making process without focusing on its inner workings. Both concepts contribute to building trust and transparency between humans and AI, which is essential for a positive relationship with this technology.

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