Developing Ethical Algorithms for Placement Stability in the Foster Care System
Student: Charlie Repaci
Mentor: Dr. Shion Guha and Devansh Saxena
The goal of this project is to use a human-centered approach grounded in current social science theory and frameworks to add context to and further develop existing placement stability and risk assessment models that are used to aid overworked social workers in making, explaining, and standardizing their decisions.
From the project page:
This project aims to collaborate with the WI Department of Children and Families and SaintA by utilizing important, useful and contextual caseworkers judgement that are recorded as detailed case notes but never actually used to add norms, values and context to existing algorithms that determine placement stability. Topic modeling will be used to extract latent themes from such text and incrementally added to existing placement stability models to test improvements in outcomes.
- Perform a literature review of topic modeling usage in the social science domain
- Understand latent and human context from caseworker notes in Wisconsin foster care system using the data provided from the mentor
- Review algorithmic biases, fairness, and transparency in the foster care system
- Evaluate the latent themes from text and incrementally add to existing placement stability models to test improvement in outcomes