Dr. Salvatore Ruggieri, Professor Dr. Dino Pedreschi, and Professor Dr. Franco Turini, all of Università di Pisa Dipartimento di Informatica, have published Integrating Induction and Deduction for Finding Evidence of Discrimination, 18 Artificial Intelligence and Law No. 1, pages 1-43 (2010). Here is the abstract:
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.
Tags: Artificial intelligence and law, Automated extraction of legal classification rules, Automated extraction of legal classifications, Automatic detection of discrimination, Automatic detection of illegal discrimination, Automatic detection of patterns of illegal discrimination, Consumer credit information systems, Defenses to discrimination, Dino Pedreschi, Discrimination, Discrimination in consumer credit, Employment information systems, Franco Turini, Insurance information systems, Legal data mining, LP2DD, Modeling affirmative action, Modeling contexts of discrimination, Modeling defenses to allegations of discrimination, Modeling legal argumentation, Modeling legal arguments, Modeling legal logic, Modeling legal rules, Modeling legally protected classes of individuals, Modeling regulations, Modeling statutes, Mortgage information systems, Salvatore Ruggieri, Statistical methods in legal informatics
