AI & Data Science

Why Data Science?

I view AI and data science as critical and emerging tools to be mobilized for the advancement of social work research and practice. The use of advanced analytics and data visualization to produce insights can be the foundation for innovative data-driven decision-making interventions, exploratory research methods, and the preparation of social work students who will inevitably work in data-filled practice environments. Rather than acting as a replacement for causality-focused statistical research techniques or human clinical judgment, data science can complement these efforts through encouraging the pragmatic use of data in policy and practice decision-making contexts. Further, I view learning, implementing, and teaching such techniques as a professional obligation to ensure that those in the helping professions are prepared to be competent and ethical agents of change in the era of AI and Big Data.

However, I also believe that data science applications must be designed and implemented carefully to ensure that they do not amplify existing inconsistencies, inequities, and inaccuracies in high-stakes decision-making contexts. This includes being thoughtful about (1) the use cases in which they may be most impactful, (2) potential systematic biases in practice or data entry that might compromise data quality, and (3) the extent to which professionals are prepared and supported in their adoption and use of such technologies. To this end, I believe that social workers should be leaders in this field of study to ensure that, as stated by Shyam Sankar in a recent TED talk, human services systems "design the human into the process" and create maximally impactful and ethical tools. I aim to conduct research during my career that will help the social work profession be prepared for this role, with an emphasis on interventions and implementation strategies that best facilitate effective and equitable adoption and use of advanced data tools.

I first became acquainted with the potential for data science in social work through participation in a project that used predictive analytics to support youth deemed most likely to age out of foster care during my time in practice. I later intentionally sought out coursework, research projects, and practice opportunities to provide me with the skills necessary to work and speak confidently in this increasingly-relevant field. I have specifically worked on projects related to the implementation of data-driven decision-making technologies in child welfare and child mental health systems to explore the challenges of technology adoption, the training of predictive algorithms and natural language processing models for research insights, and the processing and visualization of data for the improvement of mental health services administration and practice.