Artificial intelligence, digital social work, ICT, social exclusion, social services


Technological developments based on Artificial Intelligence (AI) and empirical science in all areas of society are opening new opportunities for social work and social inclusion programs. AI relies on Big Data management systems, which in turn provide opportunities for descriptive inference and preventative measures, as well as data-informed decision making.

This article outlines the characteristics of Big Data and describes the process of designing a tool for diagnosing social exclusion, the SiSo scale. The tool consists of a scale that uses 25 variables to assess situations of social difficulty on the inclusion-exclusion spectrum. It is currently being used in the social services department of one of Spain’s seventeen Autonomous Regions. The SiSo scale has the potential to advance the design of a Big Data system for social inclusion programs, provided we ensure the quality of the data. To this end, this study analyzes the suitability of the SiSo tool for measuring situations of social difficulty by conducting a Categorical Principal Components Analysis (CATPCA) and a Linear Principal Component Analysis.

The findings of the study confirm the tool’s suitability and value for measuring levels of risk for social exclusion, as well as the feasibility of implementing a system based on data generated by social inclusion programs. This article also highlights the opportunity that Big Data provides to generate knowledge by and for social work.

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