**Unlocking AI's Black Box: Introducing the "AI Transparency

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Unlocking AI's Black Box: Introducing the "AI Transparency Tiers" Framework

As AI models continue to permeate our lives, understanding how they make decisions has become a pressing concern. To address this, researchers have proposed a framework that categorizes AI decision-making processes into three tiers: Explainable AI (EAI), Transparent AI (TAI), and Accountable AI (AccAI). This framework serves as a guiding principle to strike a balance between AI autonomy and human oversight.

Tier 1: Explainable AI (EAI) - The Interpretability Tier

EAI provides insights into the reasoning behind AI decisions through interpretable models. Techniques like feature attribution, partial dependence plots, and SHAP values help identify the contributing factors to a prediction. This tier is ideal for applications where understanding the decision-making process is crucial, such as:

Medical diagnosis: Identifying the symptoms and diagnostic tests that led to a specific diagnosis.
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