Factors affecting the uptake of Natural language processing within the New Zealand Law System

Abstract or Summary

Many legal appeals and applications happen on a year to year basis and are continuing to grow. This research focuses on New Zealand as the researcher is in New Zealand and participants involved in the research are in New Zealand. The issues include the involvement of NLP in Law, integrating technology into Law, Examples of integrating technology into Law, privacy concerns, what is NLP and machine learning. The research aims to discover the challenges of integrating technology and Law based on the opinions of lawyers, discover the challenges for cases taking longer to process and shorten the workload through NLP and discover the current state of where NLP is. Contribute to providing an example of adding technology into New Zealand Law, contribute to providing the challenges of adding technology into New Zealand Law. The researcher adopts a quantitative research method for this thesis. The research model for this research is a modified TAM model. Data was collected via an online survey tool called Qualtrics. The survey has 27 survey questions: a mix of Nominal, Interval, and No Measurement as they are open-ended questions. What was found is legal professionals will gladly try any technology and provide their options on this technology. The most desired features are Templates and automation. Results found is legal professionals would gladly provide their options on this technology. There is a feeling of more information needed from participants to find the gap as to why some don’t think it could be used. This lack of information also seems to show due to participants expressing their thoughts through a survey, and they do not need to explain themselves more clearly compared to face to face interviews. The Chi-Square showed that there is a relationship between the type of practice and their experience getting day-to-day tasks done and type of practice and level of degree; however, this changes when it comes to day to day tasks done and type of practice comes to post-test as there are fewer participants. There appears to be a relation between age and years of retention/practice and age and their experience learning the technology. ANOVA showed that age is significant when it is concerning years of retention/practice.