# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 1 Introduction
### Motivation and Purpose of the Review  
The motivation behind our review stems from a nuanced understanding of the existing literature surrounding interpretable ML and AI. While there are commendable reviews addressing interpretability within specific domains, such as healthcare [4], and in specific areas of AI such as deep neural networks [5], and also in general machine learning [6; 7], our review aims to contribute a distinctive perspective. While acknowledging the shared goal among these reviews--to pursue interpretability in AI and ML--our work distinguishes itself through a comprehensive exploration of foundational interpretation techniques in AI. Moreover, it contributes to the scholarly discourse by providing a historical context, delving into publication trends that have shaped the landscape of interpretable AI over time. In essence, our review offers a holistic understanding that goes beyond domain-specific nuances, revealing the broader evolution and foundational principles underpinning interpretability in the realm of AI and ML. More specifically, our review introduces a structured categorization of interpretation techniques, encompassing model-based, representation-based, post hoc, and hybrid methods. The retrospective analysis and publication trends featured in our review reveal the evolution of different types of interpretations in AI, showcasing a preference shift towards data-driven approaches. The inclusion of a retrospective analysis allows for a deeper understanding of the evolution of interpretation in AI, and the analysis of publication trends sheds light on the evolving landscape of interpretability research. Overall, our review not only fills specific gaps in the existing literature but also enhances it through a unique consolidation of structured categorization, retrospective analysis, and publication trends, thereby shedding light on the advancements, challenges, and emerging trends in this dynamic field of explainable AI (XAI) interpretation techniques. This comprehensive review caters to researchers, practitioners, and policymakers seeking a holistic understanding of the interpretability landscape in ML. It specifically targets professionals and researchers in the fields of AI, ML, data science, and related disciplines, offering an in-depth exploration of the development and application of XAI techniques. Moreover, it provides valuable insights into the historical context and evolution of these methodologies, making it a pertinent resource for policymakers and individuals concerned with the ethical and regulatory implications of AI. By emphasizing the pivotal contributions of the original authors, this review aims to provide a thorough understanding of the foundational principles of interpretability and transparency in AI systems. Its primary objective is to facilitate a deeper comprehension of the historical significance and progression of interpretability techniques within the field. This review not only offers a comprehensive overview of foundational interpretability techniques, highlighting the key contributions made by prominent and pioneering authors, but also seeks to address the following critical questions, including a research question centered on trend analysis:  
1. What are the foundational interpretation techniques in the AI domain, and how have they influenced the development of contemporary interpretation methodologies? 2. How do diverse interpretation techniques, including model-based, representation-based, post hoc, and hybrid methods, address the challenge of explainability in AI models, thereby fostering effective human-AI collaboration and regulatory compliance? 3. What are the inherent strengths and limitations associated with the utilization of various interpretation techniques in real-world applications, particularly across different domains such as images, text, and tabular data? 4. How has the adoption of advanced computational methods within various categories of interpretation techniques influenced the evolving landscape of AI interpretability over time?