Algorithmically guided human decision making in criminal justice and beyond
Abstract/Contents
- Abstract
- With the proliferation of modern data infrastructure and computational resources, increasingly more decisions in almost every sector of public policy, including criminal justice, are informed by data-driven algorithms today. However, due to the high-stakes nature of decisions in criminal justice, the efficacy and equity of algorithms in judicial procedures are sometimes challenged and how algorithms can be appropriately incorporated into judicial processes remains a complex question to answer. In this dissertation, we present both applications demonstrating how algorithms may help human decisions in criminal justice and algorithmic tools for human-centered decision making. We start by showing risk assessment tools, one of the most common algorithm applications in criminal justice, can outperform humans on predicting recidivism under ecologically valid settings in a randomized controlled experiment. We then demonstrate how both predictive and non-predictive algorithms can help with better human decision making in criminal justice by reducing incarceration time and mitigating racial bias in two real-world applications. Finally, inspired by decision making problems encountered in aforementioned applications, we propose two algorithmic methods that aid human decision making through modeling the structure of evolving probabilistic predictions and estimating the preference of the human decision maker while searching for optimal policy configurations during sequential experimentation. Together, this dissertation contributes to the understanding of interplay between humans and algorithms in criminal justice and other domains by presenting an interconnected collection of randomized controlled experiments, real-world applications, and innovative computational methods.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Lin, Zhiyuan |
|---|---|
| Degree supervisor | Bernstein, Michael S, 1984- |
| Degree supervisor | Goel, Sharad, 1977- |
| Thesis advisor | Bernstein, Michael S, 1984- |
| Thesis advisor | Goel, Sharad, 1977- |
| Thesis advisor | Brunskill, Emma |
| Degree committee member | Brunskill, Emma |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zhiyuan (Jerry) Lin. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/mh437pw1231 |
Access conditions
- Copyright
- © 2021 by Zhiyuan Lin
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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