Authors - Lavanya K, Srinidhi G A Abstract - The pace with which artificial intelligence (AI) has been adopted in decision-critical applications has, in turn, elevated the need to have more than merely accurate AI systems that are also transparent and comprehendible. Although the complex machine learning models can be highly predictive, its black box strategy creates a question mark on the aspects of trust, accountability, and usability in real-world systems based on artificial intelligence. This paper examines the tradeoff between accuracy and transparency in interpretable machine intelligence and oranges by pointing to the trade-offs that exist between predictive accuracy and model explanation. There is a proposed structured framework which is used for comparing and investigating the black-box and interpretable models on the basis of quantitative performance measures and explainability measures. The article highlights the importance of explainable AI methods of post-hoc in improving the transparency of models without affecting the accuracy of the model significantly. Using a systematic assessment, the paper shows that interpretable machine intelligence may be used to help make reliable decisions and maintain competitive predictive performance. The results help in the creation of credible AI-based systems as it provides information about the creation of models that are effective in balancing the accuracy and interpretability when applied to different application settings.
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