# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 1 Introduction  
The growing demand for transparency and accountability in AI decision-making has made eXplainable Artificial Intelligence (XAI) an increasingly important area of research. Although interpretation techniques are not essential for the operation of AI models, they can be viewed as supplements that introduce an extra layer of transparency and explainability. This supplementary aspect could prove pivotal in ensuring the adoption and success of AI applications and the capacity of being more responsive to the needs of intended users.  
In the context of the escalating significance of AI, [PERSON] et al. [1] propose that interpretability entails grasping the mechanics of a system without necessarily plunging into causality (i.e., comprehending how it operates), whereas explainability involves deciphering the reasons (i.e., understanding why it operates). These interpretations heighten the comprehensibility, appropriateness, and usability of AI-generated guidance for itsintended users. Consequently, techniques for interpretation have emerged, aiming to facilitate human comprehension of the underlying rationale behind a model's conclusions. This becomes notably vital in high-stakes domains such as healthcare, criminal justice, environment, and finance. Instances where users fail to apprehend the logic underpinning a model's suggestions can undermine trust in its decisions, potentially leading to user exaggeration, limited adoption, and even ethical and legal dilemmas.  
From a technical perspective, integrating interpretability into a model serves several essential purposes. It aids developers in identifying and rectifying biases within the training dataset, allowing for targeted corrective measures. Moreover, interpretability plays a pivotal role in fortifying model robustness by shedding light on potential adversarial perturbations capable of altering model outputs. Additionally, it ensures that the model relies solely on relevant variables to derive its outputs, as underscored by [PERSON] et al. [2] in their work on explainable AI. In a retrospective analysis, [PERSON] et al. [3] delve into the objectives, accomplishments, challenges, and insights of the XAI program.  
In the context of XAI, a variety of interpretation techniques emerge, encompassing representation-based, model-based, post hoc, and hybrid approaches. These strategic approaches are skillfully developed to shed light on the decision-making processes of AI systems, ultimately contributing to improved human comprehension and trust in their outputs. This advancement holds immense promise for enhancing the effectiveness and dependability of AI applications across diverse sectors like healthcare, finance, and legal systems. Consequently, XAI research aims to leverage AI's capabilities while concurrently furnishing comprehensible explanations for its outcomes. These explanations might pertain to the data or systems themselves, or the decision-making mechanisms grounded in fitted models. This review conducts an in-depth exploration of the principal techniques within the XAI domain, while also addressing the challenges and opportunities inherent in their implementation.