Project recon : a computational framework for and analysis of the California parole hearing system
Abstract/Contents
- Abstract
- Parole decisions can tip a sentence toward fifteen years or fifty. Despite the great power that parole boards hold, their decision processes are poorly documented and largely hidden from public scrutiny. Parole hearings produce almost no structured data, only an unstructured transcript of hearing dialogue several hundred pages in length. In the following dissertation, we use natural language processing to analyze the transcripts of 35,105 parole hearings held between 2007 and 2019 for candidates serving life sentences in California, totaling approximately five million pages. Through regression analyses of data extracted from the transcripts, after controlling for relevant case factors, we find that several factors outside of the candidate's control explain hearing outcomes. We find that commissioners vary widely in their punitiveness in previously unobserved ways; the assignment to a particular commissioner significantly influences the hearing outcome. Racial disparities limit the quality of legal representation that parole candidates receive as well as their voice in the hearing dialogue, and both significantly predict the parole outcome after again controlling for case factors. Previous analyses of parole systems have been limited by the unavailability of structured data or the task of hand-annotating hearing transcripts. Our results thus provide the most comprehensive picture of a parole system studied to date. While our results carry direct implications for legislative parole reform, our methodology—using machine learning to analyze legal hearings—can be extended to many other procedures in criminal and administrative law with limited structured data.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Hong, Yun |
|---|---|
| Degree supervisor | Manning, Christopher D |
| Degree supervisor | Ugander, Johan |
| Thesis advisor | Manning, Christopher D |
| Thesis advisor | Ugander, Johan |
| Thesis advisor | Ashlagi, Itai |
| Thesis advisor | Goel, Sharad, 1977- |
| Degree committee member | Ashlagi, Itai |
| Degree committee member | Goel, Sharad, 1977- |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jenny Hong. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/xn213ms8118 |
Access conditions
- Copyright
- © 2023 by Yun Hong
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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