Interpretation and Visualization Techniques in AI Systems and Applications

This articulation depicts a kaleidoscope of interpretable artificial intelligence (AI). It starts with an introductory briefing on artificial intelligence. It follows the fundamental representation of the variety of AI approaches, especially the explainability and interpretability, along with the due importance of the black box model. Visualization of AI is discussed briefly, and the correlation of the same with interpretability is presented. Many types of visualization metrics and techniques are explained, followed by the very importance and need for visualization with respect to AI. Further, all reportedly available evaluation strategies are also discussed. Different metrics in terms of complexity, fidelity, understandability, coverage, and consistency are analyzed with respect to quantification. The solo and combined applications of visualization and interpretable AI are presented systematically. Finally, the challenges and corresponding prospects pertaining to visualization and interpretable AI are described for fruitful readability.