Professional Practice and Artificial Intelligence in Child Welfare Governance

University Of Chicago, Chicago IL

Investigators

Abstract

This research studies how artificial intelligence surveillance shapes social work practices. The goal of the study is to contribute to an understanding of how artificial intelligence is re-shaping social service decisions and practices. The study focuses on the application of algorithms for the evaluation of fidelity to evidence-based practices through interactions between human workers and non-human actors. The findings of this research inform reforms in the use of surveillance technologies in social services to improve the interaction between child welfare workers and AI systems. Combining theories of governance, social work, and science and technology studies, through partnership with local child welfare organizations, the research is conducted by interviewing workers, observing their work, and collecting and analyzing documents to understand the conditions structuring, practices of, and stakes of child welfare organizations using artificial intelligence to meet new accountability standards. The findings contribute to understanding how to legally stipulate what counts as evidence of effectiveness and under what conditions to incentivize, regulate, and monitor artificial intelligence. For practitioners and administrators, findings elucidate how artificial intelligence shapes workers’ relationships with families and other workers, as well as the daily work of child welfare. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.