A Computational Decision-Tree Approach to Inform Post-Conviction Intake Decisions

Abstract

How might data analytic tools support intake decisions? When faced with a request for post-conviction assistance, innocence organizations’ intake staff must determine (1) whether the applicant can be shown to be factually innocent, and (2) whether the organization has the resources to help. These difficult categorization decisions are often made with incomplete information (Weintraub, 2022). We explore data from the National Registry of Exonerations (NRE; 4/26/2023, N = 3,284 exonerations) to inform such decisions, using patterns of features associated with successful prior cases. We first reproduce Berube et al. (2023)’s latent class analysis, identifying four underlying categories across cases. We then apply a second technique to increase transparency, decision tree analysis (WEKA, Frank et al., 2013). Decision trees can decompose complex patterns of data into ordered flows of variables, with the potential to guide intermediate steps that could be tailored to the particular organization’s limitations, areas of expertise, and resources.

Keywords:

• Exonerations,
• Post-Conviction,
• Machine Learning,
• Decision Trees,
• Latent Class Analysis,
• National Registry of Exonerations,
• Decision-Making,
• Innocence Network,
• Wrongful Conviction