Abstract
This paper looks at a common law legal system as a learning algorithm, models specific features of legal proceedings, and asks whether this system learns efficiently. A particular feature of our model is explicitly viewing various aspects of court proceedings as learning algorithms. This viewpoint enables directly pointing out that when the costs of going to court are not commensurate with the benefits of going to court, there is a failure of learning and inaccurate outcomes will persist in cases that settle. Specifically, cases are brought to court at an insufficient rate. On the other hand, when individuals can be compelled or incentivized to bring their cases to court, the system can learn and inaccuracy vanishes over time.
| Original language | English (US) |
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| Title of host publication | CSLAW 2022 - Proceedings of the 2022 Symposium on Computer Science and Law |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 109-117 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781450392341 |
| DOIs | |
| State | Published - Nov 1 2022 |
| Event | 2022 ACM Symposium on Computer Science and Law, CSLAW 2022 - Washington, United States Duration: Nov 1 2022 → Nov 2 2022 |
Publication series
| Name | CSLAW 2022 - Proceedings of the 2022 Symposium on Computer Science and Law |
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Conference
| Conference | 2022 ACM Symposium on Computer Science and Law, CSLAW 2022 |
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| Country/Territory | United States |
| City | Washington |
| Period | 11/1/22 → 11/2/22 |
Funding
∗This work began during the IDEAL Spring 2021 Special Quarter on Data Science and Law organized by Jason Hartline and Dan Linna. Many thanks to John McGinnis and the anonymous referees for their valuable comments and helpful suggestions. The work was supported in part by NSF award CCF-1934931. The full version of this paper is available at https://arxiv.org/abs/2209.02866.
Keywords
- common law
- online learning
ASJC Scopus subject areas
- General Computer Science
- Law