The art of Prolog (2nd ed.): advanced programming techniques
The art of Prolog (2nd ed.): advanced programming techniques
From logic programming to Prolog
From logic programming to Prolog
The evaluation of legal knowledge based systems
ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Role of Logic in Computational Models of Legal Argument: A Critical Survey
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
IEEE Transactions on Knowledge and Data Engineering
Induction of defeasible logic theories in the legal domain
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Suppressing Data Sets to Prevent Discovery of Association Rules
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Expert Systems with Applications: An International Journal
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Computational Statistics & Data Analysis
Discrimination-aware data mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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We present a reference model for finding (prima facie) evidence of discrimination in datasets of historical decision records in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. We formalize the process of direct and indirect discrimination discovery in a rule-based framework, by modelling protected-by-law groups, such as minorities or disadvantaged segments, and contexts where discrimination occurs. Classification rules, extracted from the historical records, allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law groups is evaluated by formalizing existing norms and regulations in terms of quantitative measures. The measures are defined as functions of the contingency table of a classification rule, and their statistical significance is assessed, relying on a large body of statistical inference methods for proportions. Key legal concepts and reasonings are then used to drive the analysis on the set of classification rules, with the aim of discovering patterns of discrimination, either direct or indirect. Analyses of affirmative action, favoritism and argumentation against discrimination allegations are also modelled in the proposed framework. Finally, we present an implementation, called LP2DD, of the overall reference model that integrates induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools. The LP2DD system is put at work on the analysis of a dataset of credit decision records.