Artificial Intelligence and Organizational Memory in Government: The Experience of Record Duplication in the Child Welfare Sector in Canada

Abstract

In recent years, the topic of artificial intelligence in government has become a major area of study. Governments have been eager to adopt artificial intelligence for a number of purposes, including for the prediction of risk in social services. Child protection services are exploring predictive analytics for the initial screening of cases. While research identifies data quality issues as a major barrier, little is known about the characteristics of these issues in child protection, their relationship to organizational memory contained in administrative data, and their impact on the ability of an organization to adopt these technologies. This study gained insight into the socio-technical limitations of duplicate records when trying to bring organizational memory to bear in predictive decision support by interviewing and observing staff use of information technology systems. The study's findings suggest that record duplication in case management systems in child protection could pose a significant challenge to the introduction of artificial intelligence technologies such as predictive analytics for decision assistance. There is a need to address foundational information management and system issues before artificial intelligence approaches such as this can be introduced in the child protection sector.