Interpretability <i>vs</i> . Efficiency in ApplicationSpecific Analytics

Abstract

Interpretability in machine learning models is explored in this systematic literature review. This review covers a wide range of topics, including healthcare, social media analysis, fake news detection, self-driving vehicles, etc. It examines the importance of interpretability in these domains and highlights the ethical implications associated with black box models. Moreover, the review discusses efforts made to understand and explain complex models using techniques and tools.&lt;br&gt;&lt;br&gt;Through an analysis of selected research articles from the past decade, the review reveals the impact of interpretability on model performance and decision-making. It identifies highly interpretable models, moderately interpretable models, and black box models, along with their respective applications and outcomes. The review emphasizes the need for ongoing research to maintain a balance between accuracy and interpretability. This systematic literature review demonstrates that interpretability plays a crucial role in domains where accountability, trust, and transparency are paramount. Providing insights for future research, the review describes the benefits and challenges associated with interpretability in machine learning models.

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