Ethical Algorithms for Placement Stability in the Foster Care

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Title: Developing Ethical Algorithms for Placement Stability in the Foster Care System in Wisconsin

Mentor: Dr. Shion Guha

Approach: Understand latent and historical human context from caseworker notes on foster care cases by using topic modeling techniques. These contexts will be added to existing risk assessment models that are currently used by the state to understand placement stability. Students will be exposed to topic modeling and natural language processing techniques in addition to learning theory that centers around algorithmic biases, fairness and transparency. Summary: Foster care systems in the US are generally underfunded and overburdened. Increasingly, algorithms are used to make decisions in foster care, ranging from placement stability to initial risk assessment to even estimating foster parent matching and payments. However, there is significant empirical evidence suggesting that outcomes for foster care children are unequal, unfair and biased in different contexts. In Wisconsin, the foster care system has been under a federal lawsuit since 1993 for such biases and one main objective is to improve placement stability. 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.

Student Research Activities: The REU fellows will perform the following major tasks:

  • Perform literature review of topic modelling usage in 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.

Student Background: Students needs to have introductory programming skills, preferably in Python or R.