Developing an explainable artificial intelligence based decision support system for law cases

In this thesis study, an explainable artificial intelligence-based system has been developed to support lawyers with effective decision support in law cases. Law cases correspond to events that involve a wide variety of parameters and scenario situations. Because of this, it is a process requiring an intensive effort for lawyers to focus on more than one case file and progress effectively. At this point, although various computer software systems have been developed to track cases in a digital environment and manage the whole process, especially the decision support functions provided by artificial intelligence algorithms have triggered the emergence of innovative software solutions in lawful applications. As appropriate with this situation, the system developed in this thesis study offers lawyers artificial intelligence-based outputs on three different scenarios: determining the law case type, predicting the probability of winning the case, and determining the next step to be taken in the process. The suggested system was also made reliable by ensuring a structure based on explainable artificial intelligence functions. The performed applications show that the software offers lawyers an effective decision support solution.