Computational Social Welfare: Applying Data Science in Social Work

Computational social welfare, a powerful new science, combines a focal commitment to social justice and equity with adoption of computational modeling as an epistemological paradigm and with advanced data science skills as the methodology. As a science focused on learning from data, it mirrors the values and processes of grounded theory already well established in social welfare and it elevates the use of administrative data, which is so prevalent in the social settings of interest to social welfare scholars. As a science deeply rooted in complexity theory, it promises to produce new insights about the complex and adaptive social environments in which social workers practice and conduct research. As an inherently cross-disciplinary science, it welcomes new perspectives about how to understand and solve social problems. As a science led by innovations and one in use outside of universities with later adoption by academic researchers, it provides a template for social welfare to embrace an action-oriented research agenda led by practitioners and communities. In this way, it aligns well with the participatory paradigm already embraced by many social welfare scholars. As a science that promotes transparency and open access, it facilitates a critical paradigm that can challenge oppressive beliefs and practices embedded in traditional, historical, and legacy research traditions. Computational social welfare is situated within the umbrella of computational social science. It is analogous to computational approaches in other fields, including computational biology, computational linguistics, computational finance, and computational cognition. All computational approaches exist within the broader domain of computational science, understood to be a science that uses networks, computers, software, algorithms, and simulations to create new knowledge. Please refer to the separate Oxford Bibliographies in Philosophy article “Computational Science” for more information. Computational social welfare also benefits from technology development. Technology innovation provides a foundation for computational social welfare. However, computational social welfare focuses more on application and analysis than hardware development. Please refer to the separate Oxford Bibliographies in Social Work articles “Technology in Social Work” and “Technology for Social Work Interventions” for more information about technology in social work. Computational social welfare seems like a science well suited for solving modern social challenges. However, it has not yet been widely embraced and tested by social welfare scholars. Therefore, this article aims to introduce the various facets of computational social welfare to practitioners and scholars dedicated to social well-being with a goal of advancing its use and testing. It is generally focused on the field of social welfare but will be of interest to those involved in, and it draws citations from, fields that share a commitment to improving conditions for people, including but not limited to public policy, sociology, economics, nursing, education, criminal justice, public health, psychology, and political science. Social work practitioners can learn how data science is applied in other disciplines for social well-being, including trends, argument, methods, and analysis, that could inspire social welfare scholars to enhance the social work discipline.