AI Explains Itself: Zero-Shot Textual Explanations from Feature Translation

This research explores a novel method for AI to explain its decision-making process without requiring specific training examples. By translating decision-critical features into textual explanations, this work promises to improve the transparency and interpretability of AI models.

• •Addresses the challenge of AI 'black box' behavior by providing explanations.
• •Utilizes a zero-shot approach, reducing the need for extensive training data.
• •Focuses on translating critical features to generate textual explanations.