CustodiAI: A System for Predicting Child Custody Outcomes
Journal
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Long Papers, IJCNLP-AACL 2023
Journal Volume
5
Start Page
10
End Page
16
ISBN (of the container)
979-889176016-5
ISBN
[9798891760165]
Date Issued
2023-11
Author(s)
Abstract
Predicting child custody decisions post-divorce is crucial but challenging due to numerous nonnumerical, text-based factors, particularly in joint custody scenarios. This study presents the Intermediate Self-Supervised Training (ISST) method, a two-stage approach that classifies document paragraphs using original rationale labels before leveraging this to predict custody at the document level. Achieving up to 90.57% accuracy and notably, a 78.95% F1-score for joint custody cases, it surpasses previous models by 13%. We further refine the model to mitigate gender bias in the training data and provide error estimations, enhancing fairness and reliability. Our user-friendly online system exemplifies our model's applicability in out-of-court dispute resolution, potentially reducing time and financial strains for families in crisis.
Event(s)
13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, IJCNLP-AACL 2023
Publisher
Association for Computational Linguistics
Type
conference paper