# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 3 Current XAI State of the Art
### Explanator Formats
#### 3.1.2 Textual Explanations  
Textual explanations in Artificial Intelligence refer to sentences presented in natural language, which try to provide human-readable narratives of the model's decision-making process. This aspect makes them suitable for non-technical stakeholders, as the explanations describe, at a certain level of natural language, why a model made a particular prediction or decision. Technically, the approach provides human users of AI systems with clues about the process behind the decision of any ML model, such as the labels assigned to image parts in an image classification task [54]. Table 1 is an example chosen from the literature of the generation of textual explanations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Task** & **Explanation Example** & **Reference** \\ \hline Animal’s image classification & & has\_eye\_color = **black** \\  & has\_underparts\_color = **white** \\  & has\_belly\_color = **white** \\  & has\_breast\_color = **white** \\  & has\_breast\_pattern = **solid** \\ \hline \hline \end{tabular} \
\begin{tabular}{c} \hline \hline  \\ \end{tabular}
\begin{tabular}{c} \hline \hline We guess you would like to watch Terminator 2: \\ Judgement Day (1991) more than Transformers: \\  & Revenge of the Fallen (2009) because you may prefer: \\  & \(\bullet\) (subject) 1990s science-fiction films \\  & \(\bullet\) (subject) Science-fiction adventure films \\ Movie & \(\bullet\) (subject) Films using computer-generated imagery \\ Recommendation & \(\bullet\) (subject) Drone films \\  & \(\bullet\) (subject) Cyberpunk films [56] \\ Prediction: \\ Terminator 2 & Over: \\  & \(\bullet\) (subject) Films set in Egypt \\  & \(\bullet\) (subject) Robot films \\  & \(\bullet\) (subject) Films shot in Arizona \\  & \(\bullet\) (subject) Ancient astronauts in fiction \\  & \(\bullet\) (subject) IMAX films \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of textual explanations as found in the XAI literature.  
Figure 3: Example of a saliency map. The pictures on the right are the saliency maps of the left image and show the regions that are more attentive parts for the CNN. (Source: geeksforgeeks.org/what-is-saliency-map; accessed on 25 January 2025).  
The key methods used to generate textual explanations include the following:  
**LIME** (Local Interpretable Model-Agnostic Explanations) [20]: Generating basically mixed explanations, LIME generates local surrogate models that approximate the decision boundary of a black-box model in a specific region of interest. It then provides a text in a natural-language format, aiming to describe the important features in that region. This makes it easier for humans to understand why the model made a particular decision.  
**SHAP** (Shapley Additive Explanations) [21]: Although this method generates more than just texts, as will be shown later, SHAP provides a game-theoretic approach to explain the output of any machine learning model. By computing Shapley values, the method determines the contribution of each feature to a model's prediction and provides textual explanations that describe the impact of each feature.  
Various other methods for generating textual explanations exist. These include, but are not limited to, Rationales [58], InterpNet [59], Relevance Discriminative Loss [60], Most-Weighted-Path, Most-Weighted-Combination and Maximum-Frequency-Difference [61], and Neural-Symbolic Integration [62].