**5. Discussion**

As explained in the Introduction, this research focuses on operations for disaster response, where information has to be concise and arrives on time for decision-makers at the operational level to make an informed decision. The selected case studies are only a subsample of the different scenarios investigated in this work. The final output for each query was four organizations, and four studies were selected (**Figure 8**). These texts belong to the set of texts grouped in the BMU assigned to each query.

A correlation with the specific interest can be observed when analyzing the final output. Let us focus on the first case study. Here we can see that the keywords extracted from the first query (2016 Earthquake of magnitude 7.8 on the Ecuadorian coast) are: earthquake, people, severely, assistance, shelter, health care, facility; and that those assigned to the BMU were: community, earthquake, risk, safety; have several overlapping, which demonstrates the consistency in the clustering. Within the selected cell were various humanitarian organizations and academic publications from different research fields such as health sciences, engineering, and architecture. These are all clustered together as they share similar keywords and concerns. This pattern will always occur; an output will always suggest a list of possible options from different fields of study. Therefore, the final selection, which can be called human

*The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response DOI: http://dx.doi.org/10.5772/intechopen.108196*


#### **Figure 8.**

*A list of organizations and tools from the cluster text that belongs to the BMU closest to each query vector.*

supervision, ensures the success of the articulation. The decision-maker decides what kind of information is relevant for the decision-making based on particular concerns. In our case, as we focus mainly on architectural practices, the tools and organizations shown in the final outputs (**Figure 8**) are the ones with constructive and spatial focus.

The present experiment described a methodology that joined two discourses from the field of disaster response together to create a Common Ground, which then provided a ground for selecting and prioritizing information regarding a specific interest to articulate three final outputs. When working with a data-driven approach, there are often questions about whether the accuracy of the results can be trusted. To avoid this, the research proposed a methodology involving human and artificial intelligence interplay where accuracy is ensured by a series of filters that are user-dependent and secure the specificity of the result.

### **6. Conclusion and future work**

First, several lists of humanitarian organizations indexed on the web contain information that cannot be compared; in other words, most organizations registered in one source are not present in another. To settle on a reliable source, one must navigate and filter through to get a representative number. Second, several approaches from different disciplines share similar keywords, e.g., academic writing from the field of health with the field of building safety assessments. Though both are extremely necessary, their approaches are entirely different. Therefore, even if AI predicts similarities among them, humans must be present to make the final decision and selection.

Additionally, it was found that there is a lack of research on how to integrate AI into a workflow of large-scale disaster response, especially in countries with scarce resources. Therefore, in the future, we should look for ways to apply the proposed or similar methodologies to an ongoing disaster case study to validate the speed and relevance of the results. Besides, it would be interesting if researchers add new discourses to those proposed in this experiment, e.g., social media, as these will bring a new stakeholder approach to disaster response. Also, researchers can examine different ways of encoding text data into numerical vectors. For example, instead of using word embedding, the frequency of words used over time (Google Books Ngram Viewer) can be used, and the results of the present research can be compared with the latter.

To conclude, AI is a problem-solving tool tailored to specific problems. Therefore, in a natural disaster scenario, this type of intelligence can be beneficial. Many problems gather a massive amount of data that require considerable computational power, and there are usually few people available to process this data. Therefore, by joining the strengths of human cognition with the strengths of AI computing, this experiment illustrates a method for creating a Common Ground where we can depict collaboration among humanitarian organizations and researchers around the world to aid an informed response in the aftermath of a natural disaster.
