Summary of End-to-end Optimization and Learning Of Fair Court Schedules, by My H Dinh and James Kotary and Lauryn P. Gouldin and William Yeoh and Ferdinando Fioretto
End-to-End Optimization and Learning of Fair Court Schedules
by My H Dinh, James Kotary, Lauryn P. Gouldin, William Yeoh, Ferdinando Fioretto
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 proposed joint optimization and learning framework uses machine learning models trained end-to-end with efficient matching algorithms to construct fair criminal court pretrial scheduling systems that account for defendants’ preferences and availability. The framework optimizes a principled measure of fairness, balancing the availability and preferences of all parties, including courts, prosecutors, defense teams, and defendants. This approach aims to improve pretrial outcomes by minimizing the costs imposed on the system and ensuring that defendants’ scheduling preferences are taken into account. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method for constructing fair criminal court schedules is proposed. The schedule takes into account the preferences of all parties involved, including courts, prosecutors, defense teams, and defendants. This helps to improve pretrial outcomes by reducing costs and making sure defendants get a fair chance to participate in the court process. |
Keywords
* Artificial intelligence * Machine learning * Optimization
