The project Artificial intelligence facing Multidimensional Poverty in Elderly (AMPEL) focuses on the use of cutting edge technologies in Artificial Intelligence (AI), Machine Learning (ML), data analysis and data visualization to identify the risk of poverty in elderly people, relying on multidimensional indicators, learned from heterogeneous sources of information.
AMPEL is a research project aiming to contribute in studies for eradicating poverty in all its forms, one of the greatest challenges that humanity has to face and is the first goal of the 2030 Agenda through the plan of action for people, planet and prosperity signed in September 2015 by the Member States of the United Nations, that incorporates the seventeen Sustainable Development Goals (SDGs) to achieve by 2030.
The main target of AMPEL focused on elderly at risk of multidimensional poverty, a novel approach in fighting poverty enhancing the comprehension of dimensions of living not merely related to economic/financial factors (incomes), but also different disadvantages afflicting longeve people.
The focus on financial resources alone does not capture people’s needs and quality of life. Being poor means ialso a lack of access to resources enabling a minimum standard of living and participation in the society. Elderly people are likely to require help with some or all everyday activities and the total costs of this help can be very high and absorb a significant amount of their income, especially when they are alone and not in good health.
The main target of AMPEL focused on elderly at risk of multidimensional poverty, a novel approach in fighting poverty enhancing the comprehension of dimensions of living not merely related to economic/financial factors (incomes), but also different disadvantages afflicting longeve people.
The focus on financial resources alone does not capture people’s needs and quality of life. Being poor means ialso a lack of access to resources enabling a minimum standard of living and participation in the society. Elderly people are likely to require help with some or all everyday activities and the total costs of this help can be very high and absorb a significant amount of their income, especially when they are alone and not in good health.
The well-defined group of persons facing poverty – the elderly – are considered to develop new methodologies to predict poverty risk indicators through the metaphor of an alert semaphore (ampel, in German), to classify three levels of susceptibility to poverty (red, yellow and green), useful to identify where a prompt reaction would be needed, especially in emergency.
The availability of a great ammount of data collected during the experience of pandemic could reveal indicators of such dimensions through the application of the innovative AI-based technologies allowing the development of realistic plans based on the envisioning of risk of multidimensional poverty.
AMPEL is based on a strongly cross-disciplinary research approach , integrating skills, methodologies and tools ranging from Computer Science, Sociology and Statistics. Experts working in different areas are collaborating to establish promising researches and results with the aim to significantly contribute to the detection of risk of poverty in elderly.
Department of Informatics, Systems and Communication
Department of Sociology and Social Research
Department of statistics and Quantitative Methods
The project is funded by Fondazione Cariplo within the Call Scientific Research 2020 Data Science for science and society
