Fighting multidimensional poverty through AI

Poverty is a multidimensional concept: the focus on financial resources alone does not capture people’s needs and quality of life. Being poor means in fact also a lack of access to resources enabling a minimum standard of living and participation in the society. The AMPEL (Artificial intelligence facing multidimensional poverty) project considers elderly people and consider data not only on income and wealth, but also on material and social deprivation that are rarely collected or known by public welfare institutions, making it difficult to intercept those who require more support.  A dataset of heterogeneous data is used to extract the indicators needed to define a poverty risk. Unsupervised models are considered to unveil hidden patterns that can identify variational factors as separate sources of influence that can be used to characterize different levels of risk. Then supervised approaches are adopted for building predictive models, and hierarchical approaches are investigated for analyzing data focusing on different views as, for example, gender or size of municipalities.