Session: New Research Methods at the Intersection of Social Work, Data Science, and Social Equity: Research from the NYU Silver School of Social Work Constance and Martin Silver Center on Data Science and Social Equity (Society for Social Work and Research 27th Annual Conference - Social Work Science and Complex Problems: Battling Inequities + Building Solutions)

The Grand Challenge for Social Work on Harnessing Technology for Social Good calls upon social work to mobilize the potential of new technologies and data sources and emerging digital resources to address complex problems and promote social good. Data science, including big data and artificial intelligence, is already transforming social work science and practice. As the field of data science continues to grow in scope and influence, the potential to shape the field and address the challenges inherent in data science for social equity will increase. Because data science approaches are designed by humans, experts argue that social workers are crucial to ensure that they promote human cooperation over competition, participation and inclusion over autonomy, and plurality and diversity over centralization and homogeneity.

Social work researchers can make important contributions to the evolving area of data science and take the lead in addressing valid concerns about using data science approaches with embedded assumptions that may have the potential to do more harm than good. We can take a leading role in centering principles of social equity, social good, and anti-racism to develop new ethical and equitable insights and make important contributions to scholarship and social policy related to data science.

The new Constance and Martin Silver Center on Data Science and Social Equity (C+M Silver Center) at NYU supports scholarship at the intersections of data science, social equity, and social work to achieve broad and transformational social impact. This symposium presents methods used in three novel research studies supported by the C+M Silver Center. The first paper addresses the need for approaches to categorize rich, unstructured data for complex indicators of clinical service quality. It uses natural language processing, an artificial intelligence method, to develop a measure of person-centered care using clinical narrative notes from behavioral health settings, which can be used for research and quality improvement purposes.

The second paper fills a gap in our understanding of the interactions among macro-contextual factors and individual-level variables on people's lived experiences of racial discrimination and psychological harm. It examines how macro-contextual factors like racial climate and structural inequalities affect the health and well-being of Asian Americans during the COVID-19 pandemic. Integrating state-level racial sentiment data constructed from Twitter data with individual-level demographic and health variables, the authors draw new insights on how racist public discourse on social media interacts with individual risk/protective factors.

The third paper addresses the need for a more nuanced understanding of the processes of aging and cognitive functioning across race and ethnicity. It applies machine learning methods to a large, multi-year dataset on health and aging to predict cognitive functioning later in life among Hispanic, Black, and White persons. The study integrates principles of anti-racism and social equity framing and minority stress theory, thus providing new insights to theory and methods.

The symposium will end with a facilitated discussion on the use of data science methods in social work research that centers social equity, as well as challenges and opportunities for the future of the field.2 on 4-18-2022-->