Inducing Predictive Models for Decision Support in Administrative Adjudication
| Autor: | Alexander S. Yeh, Elizabeth M. Merkhofer, Bradford Brown, Brandy Weiss, L. Karl Branting |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Decision support system
Computer science 06 humanities and the arts 02 engineering and technology Intellectual property 0603 philosophy ethics and religion Security token Data science Motion (physics) Consistency (database systems) Salient 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 060301 applied ethics Predictive text Adjudication |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030001773 AICOL |
| DOI: | 10.1007/978-3-030-00178-0_32 |
| Popis: | Administrative adjudications are the most common form of legal decisions in many countries, so improving the efficiency, accuracy, and consistency of administrative processes could significantly benefit agencies and citizens alike. We explore the hypothesis that predictive models induced from previous administrative decisions can improve subsequent decision-making processes. This paper describes three datasets for exploring this hypothesis: motion-rulings, Board of Veterans Appeals (BVA) decisions; and World Intellectual Property Organization (WIPO) domain name dispute decisions. Three different approaches for prediction in these domains were tested: maximum entropy over token n-grams; SVM over token n-grams; and a Hierarchical Attention Network (HAN) applied to the full text. Each approach was capable of predicting outcomes, with the simpler WIPO cases appearing to be much more predictable than BVA or motion-ruling cases. We explore several approaches to using predictive models to identify salient phrases in the predictive texts (i.e., motion or contentions and factual background) and propose a design for incorporating this information into a decision-support tool. |
| Databáze: | OpenAIRE |
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