## 4 Discussion  
Ensuring ethical, inclusive, and unbiased machine learning tools is one of the new epistemic frontiers in the application of artificial intelligence technologies to disaster risk management. We recall that this paper discusses an individual application of machine learning tools to a multi-risk assessment of a Northern Italy case study. For this purpose, we had at our disposal a massive amount of data from the ISTAT database containing indicators and data on seismic, hydrogeological, and volcanic risk as well as demographic, housing, territorial and geographical information, obtained through the integration of various institutional sources such as Istat, INGV, ISPRA, Italian Ministry for Cultural Heritage. Like all big data technologies, the adopted machine learning model proved effective in reducing CPU time and model-development costs, owing to its ability to process quantities and sources of data that could not have been otherwise simply elaborated [24; 25]. We expect that the model can be used to devise mitigation measures, prepare emergency response, and plan flood recovery measures. The proposed tool has, indeed, the potential for being an operational instrument for land use managers and planners. However, misuse should be avoided, and, for this purpose, crucial issues such as applicability, bias, and ethics should be carefully considered [24; 25; 26]. The ethical issues pertaining to a possible misuse of Artificial Intelligence technologies are several [25], including the loss of human decision making, the potential for criminal and malicious use, the emergence of problems of control and use of data and systems, the dependence of the outcomes on users' bias, and the possible prioritization of the \"wrong\" problems with respect to stakeholder expectations.  
Prioritization in disaster multi-risk management, additionally, is markedly affected by needs and expectations of private users, public agencies, and final stakeholders. For instance, a water level management company will be expectedly more inclined to consider flood risk as the most important risk to cope with, while any public agency that is called to reduce the seismic vulnerability of a certain region will tend to consider seismic risk as a priority. Thus, the labeling of the clusterization will be intrinsically permeated with the end-user's intentions. A further aspect is that one should understand that publicizing the results of a multi-risk algorithm might inadvertently touch sensitive aspects from a privacy point of view [27].  
In many cases, criticalities rely upon an inherent disconnect between the algorithm's designers and the communities where the research is conducted [26], while users may complain about a lack of transparency and accountability. Furthermore, immature machine learning tools might be used in safety-critical situations for which they are not yet ready.  
Figure 11: Population of each risk cluster by province in the Emilia Romagna region. In the \(y\)-axis, the number of municipalities has been reported.  
As suggested by [PERSON] et al. [26], disaster-risk-management specialists constantly seek expertise on how to clearly communicate the results and uncertainties of machine learning algorithms to reduce inflated expectations. Furthermore, sensitive groups should be identified and audited for overcoming bias. Therefore, we suggest that, before being systematically applied, the present machine learning methodology is validated against established computational modeling tools. We also believe that the obtained results are very promising, but further efforts are necessary to assess the proneness of the proposed machine learning tool to the aforementioned ethical and bias issues.