# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 3 Techniques for Explainable AI
### Model-Based Interpretation Techniques
#### 3.1.1 Rule-Based Models  
[PERSON]. [18] made groundbreaking contributions to the field of AI that significantly influenced the development of rule-based models. Through his visionary work on time-sharing systems, [PERSON] laid the groundwork for a more interactive computing environment, allowing researchers to engage in real-time experimentation and iterative development of rule-based reasoning and symbolic computation. Moreover, [PERSON]'s invention of the LISP programming language revolutionized the field of AI by enabling the manipulation of symbolic expressions and the implementation of complex logical rules. LISP's unique features, including support for recursion and dynamic typing, provided researchers with a powerful tool to explore and refine rule-based systems. [PERSON]'s pioneering efforts in promoting interactive computing and advancing symbolic computation were pivotal in shaping the trajectory of rule-based modeling and its subsequent applications in various domains of AI research and development.  
Rule-based models employ explicit sets of rules to analyze input data and facilitate decision-making. These rules often take the form of \"if-then\" statements, wherein the  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Technique** & **Type** & **Interpretability** & **Category** & **Data Type \({}^{*}\)** \\ \hline SHAP [15] & Agnostic & Local & Post hoc & STR, USTR, TS \\ LIME [16] & Agnostic & Local & Post hoc & STR, USTR, TS \\ Linear Models [17] & Agnostic & Local & Model-based & STR \\ Rule Extraction [18] & Agnostic & Local & Model-based & STR, Text \\ Decision Trees [19] & Agnostic & Global/Local & Model-based & STR \\ Anchors [20] & Agnostic & Local & Post hoc & STR, Text, IMG \\ Kernel SHAP [15] & Agnostic & Global/Local & Post hoc & STR, Text, IMG \\ Tree SHAP [21] & Tree-based & Global/Local & Post hoc & STR, Text, IMG \\ Deep SHAP [15] & DL & Global/Local & Post hoc & STR, Text, IMG \\ DeepLIFT [22] & DL & Global/Local & Post hoc & Image, Text \\ Grad-CAM [23] & CNN & Local & Post hoc & IMG \\ \hline \hline \end{tabular} \({}^{*}\)STR–structured data; USTR–unstructured data such as text, IMG—Image, Video, Audio; TS—time series data  
\end{table}
Table 1: Overview of Interpretation Techniques for Explainable Artificial Intelligence (XAI).  
Figure 1: Core principles for interpretable techniques. Icons source: Flaticon. (2023). [Automation, Data Modelling, Settings, Hybrid]. Retrieved from [[https://www.flaticon.com/](https://www.flaticon.com/)]([https://www.flaticon.com/](https://www.flaticon.com/)) (accessed on 1 November 2023).  
\"if\" segment outlines conditions that must be met, and the corresponding \"then\" segment prescribes the appropriate action upon satisfaction of those conditions. In essence, rule-based models offer transparent and human-interpretable rules that guide the decision-making process of AI systems.  
Such rules frequently originate from domain experts and require a well-defined problem context, featuring clear cause-and-effect relationships between inputs and outputs. An illustrative example is the domain of expert systems, where a set of rules guides decisions and dispenses advice within a specific field. These models also extend to decision support systems, aiding users in decision-making, and extend even to applications in natural language processing, where rules enable the parsing and comprehension of natural language text.  
While rule-based models excel in providing transparency and comprehensibility, their efficacy might wane in contexts requiring heightened adaptability or when intricate problem domains surpass human experts' capacity to formulate explicit rules. In such scenarios, alternative ML techniques, like neural networks or decision trees, may prove to be more suitable.