Knowledge discovery from databases (KDD) exercises in law have typically attempted to derive knowledge about decision making processes in the legal domain automatically from datasets. This is made difficult in that real data that represents aspects of a decision process in law is commonly stored as text and rarely stored in structured databases. The central claim advanced here is that KDD processes can be usefully applied to existing datasets of client and demographic data in order to provide feedback for the effective operation of organizations within the legal system. However, the cost of data mining suites and the scarcity of specialized personnel for these tools mitigates against their use. In this study data mining with Association Rules (AR) has been performed on a data-set of over 380,000 records from a legal aid agency. Methods to visualise patterns in order to suggest and test plausible hypotheses from the data have been developed. The tool, called WebAssociate is entirely web based. Domain experts using the tool report favorable responses.
Alternative Dispute Resolutions systems are not uncommon in Australian Family Law, however to date these systems are largely negotiation based and are not designed for producing judicially fair outcomes. This paper proposes an online dispute resolution approach that aims to support divorcees to resolve property issues in a manner that is consistent with orders a judge would make if the matter was heard in Court. The approach integrates a protocol for online dispute dialogue with an argument based model of judicial reasoning to structure the dispute. The likelihood of alternates outcomes is predicted with a series of Bayesian Belief Networks.