Supporting discretionary decision-making with information technology
- Hall, Mary Jean, Calabro, Domenico, Sourdin, Tania, Stranieri, Andrew, Zeleznikow, John
- Authors: Hall, Mary Jean , Calabro, Domenico , Sourdin, Tania , Stranieri, Andrew , Zeleznikow, John
- Date: 2005
- Type: Text , Journal article
- Relation: University of Ottawa Law & Technology Journal Vol. 2, no. 1 (2005), p. 1-36
- Full Text:
- Reviewed:
- Description: A NUMBER OF INCREASINGLY SOPHISTICATED technologies are now being used to support complex decision-making in a range of contexts. This paper reports on a project undertaken to provide decision support in discretionary legal domains by referring to a recently created model that involves the interplay and weighting of relevant rule-based and discretionary factors used in a decision-making process. The case study used in the modelling process is the Criminal Jurisdiction of the Victorian Magistrate’s Court (Australia), where the handing down of an appropriate custodial or non-custodial sentence requires the consideration of many factors. Tools and techniques used to capture relevant expert knowledge and to display it both as a paper model and as an online prototype application are discussed. Models of sentencing decision-making with rule-based and discretionary elements are presented and analyzed. This paper concludes by discussing the benefits and disadvantages of such technology and considers some potential appropriate uses of the model and web-based prototype application.
- Description: C1
- Description: 2003001431
- Authors: Hall, Mary Jean , Calabro, Domenico , Sourdin, Tania , Stranieri, Andrew , Zeleznikow, John
- Date: 2005
- Type: Text , Journal article
- Relation: University of Ottawa Law & Technology Journal Vol. 2, no. 1 (2005), p. 1-36
- Full Text:
- Reviewed:
- Description: A NUMBER OF INCREASINGLY SOPHISTICATED technologies are now being used to support complex decision-making in a range of contexts. This paper reports on a project undertaken to provide decision support in discretionary legal domains by referring to a recently created model that involves the interplay and weighting of relevant rule-based and discretionary factors used in a decision-making process. The case study used in the modelling process is the Criminal Jurisdiction of the Victorian Magistrate’s Court (Australia), where the handing down of an appropriate custodial or non-custodial sentence requires the consideration of many factors. Tools and techniques used to capture relevant expert knowledge and to display it both as a paper model and as an online prototype application are discussed. Models of sentencing decision-making with rule-based and discretionary elements are presented and analyzed. This paper concludes by discussing the benefits and disadvantages of such technology and considers some potential appropriate uses of the model and web-based prototype application.
- Description: C1
- Description: 2003001431
Discovering interesting association rules from legal databases
- Ivkovic, Sasha, Yearwood, John, Stranieri, Andrew
- Authors: Ivkovic, Sasha , Yearwood, John , Stranieri, Andrew
- Date: 2002
- Type: Text , Journal article
- Relation: Information & Communication Technology Law Vol. 11, no. 1 (2002), p. 35-47
- Full Text:
- Reviewed:
- Description: The Knowledge Discovery from Databases (KDD) technique called 'association rules' is applied to a large data set representing applicants for government-funded legal aid. Results indicate that KDD can be an invaluable tool for legal analysts. Association rules discovered identify associations between variables that are present in the data set though are not necessarily causal. Interesting rules can prompt analysts to formulate hypotheses for further investigation. The identification of interesting rules is typically performed using an objective measure of 'interesting' although this measure is often not sufficiently accurate to eliminate all uninteresting rules. In this article, a subjective measure of interestingness is adopted in conjunction with the objective measures. This leads to the ability to focus more accurately on those rules that surprise the analyst and are therefore more likely to be interesting. In general, KDD techniques have not been applied to law despite possible benefits because data is often stored in narrative form rather than in structured databases. However, the impending introduction of data warehouses that collect data from a number of organizations across a legal system presents invaluable opportunities for analysts using KDD.
- Description: C1
- Description: 2003000037
- Authors: Ivkovic, Sasha , Yearwood, John , Stranieri, Andrew
- Date: 2002
- Type: Text , Journal article
- Relation: Information & Communication Technology Law Vol. 11, no. 1 (2002), p. 35-47
- Full Text:
- Reviewed:
- Description: The Knowledge Discovery from Databases (KDD) technique called 'association rules' is applied to a large data set representing applicants for government-funded legal aid. Results indicate that KDD can be an invaluable tool for legal analysts. Association rules discovered identify associations between variables that are present in the data set though are not necessarily causal. Interesting rules can prompt analysts to formulate hypotheses for further investigation. The identification of interesting rules is typically performed using an objective measure of 'interesting' although this measure is often not sufficiently accurate to eliminate all uninteresting rules. In this article, a subjective measure of interestingness is adopted in conjunction with the objective measures. This leads to the ability to focus more accurately on those rules that surprise the analyst and are therefore more likely to be interesting. In general, KDD techniques have not been applied to law despite possible benefits because data is often stored in narrative form rather than in structured databases. However, the impending introduction of data warehouses that collect data from a number of organizations across a legal system presents invaluable opportunities for analysts using KDD.
- Description: C1
- Description: 2003000037
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