Abstract
Machine learning (ML) algorithms are increasingly used in high-stake domains like healthcare. While ML systems frequently outperform humans in specific tasks, ensuring safety and transparency is critical in these domains. Interpretability, therefore, plays a crucial role in understanding the decision-making process, auditing and correction of ML models and establishing trust.
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