Summary of Towards Explainability in Legal Outcome Prediction Models, by Josef Valvoda et al.
Towards Explainability in Legal Outcome Prediction Models
by Josef Valvoda, Ryan Cotterell
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method for identifying the precedent employed by legal outcome prediction models, aiming to explain their reasoning and facilitate human understanding. The approach develops a taxonomy of legal precedent to compare human judges’ and neural models’ reliance on different types of precedent. While the models predict outcomes reasonably well, their use of precedent differs from that of human judges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how AI models make decisions in law, which is important for using these tools in real-world cases. The researchers came up with a new way to see what legal principles or past cases an AI model uses when making predictions. They also created a system to categorize different types of precedent and compare it to how human judges think. While the models are good at predicting outcomes, they don’t use precedent like humans do. |
