Developing Ethical Algorithms for Placement Stability in the Foster Care System

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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.


Quoted from the project page:

This project aims to collaborate with the WI Department of Children and Families and SaintA (a private non-profit organization that actually provides foster care services in WI) by utilizing important, useful and contextual caseworkers judgment 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.


Week Description
Week 1
  • Orientation
  • Data Science Bootcamp
  • Good Research Practices Lecture
  • Begin literature review -- algorithms in social work
Week 2
  • Responsible Conduct of Research Training
  • CITI certification
  • Technical writing workshop
  • Continue literature review -- ethics and algorithms
Week 3
  • Set up wiki
  • Begin reviewing data
  • Continue literature review -- bias in algorithms and policy
Week 4
  • Import data
  • Research presentation by guest lecturer Dr. Walt Bialkowski
  • Presentation of work done in weeks 3 and 4 to mentors
Week 5
  • Research Presentations lecture
  • Data exploration and visualization
  • Technical presentations workshop and informal presentations
Week 6
  • Data Ethics by guest lecturer Dr. Michael Zimmer
  • Making Research Posters lecture
  • Continued exploratory work and visualizations to highlight important factors
  • Continue literature review -- bias in algorithm development
Week 7
  • Looking more deeply into Phase I
  • Exploratory visualizations
  • Continue literature review -- gender diversity and algorithms
Week 8
  • Exploratory visualizations
  • Continue literature review -- gender diversity and algorithms
  • Graduate Schools discussion by Dr. Brylow
Week 9
  • Industry guest panel (Northwestern Mutual Data Science Institute)
  • Begin the final paper
  • Start to create project poster
  • Exploratory visualizations
Week 10
  • Prepare and give the oral presentation
  • TBD: Present project to other REU sites and see their research in return
  • Finish and submit the final paper