Applications of Artificial Intelligence Methodologies to Behavioral and Social Sciences

Abstract

Although Artificial Intelligence (AI) has been a part of the computer science field for many decades, it has only recently been applied to different areas of behavioral and social sciences. This article provides an examination of the applications of AI methodologies to behavioral and social sciences exploring the areas where they are now utilized, the different tools used and their effectiveness. The study is a systematic research examination of peer-reviewed articles (2010–2019) that used AI methodologies in social and behavioral sciences with a focus on children and families. The results indicate that artificial intelligence methodologies have been successfully applied to three main areas of behavioral and social sciences, namely (1) to increase the effectiveness of diagnosis and prediction of different conditions, (2) to increase understanding of human development and functioning, and (3) to increase the effectiveness of data management in different social and human services. Random forests, neural networks, and elastic net are among the most frequent AI methods used for prediction, supplementing traditional approaches, while natural language processing and robotics continue to increase their role in understanding human functioning and improve social services. Applications of AI methodologies to behavioral and social sciences provide opportunities and challenges that need to be considered. Recommendations for future research are also included.