David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Artificial Intelligence and Law 18 (1):1-43 (2010)
We present a reference model for finding 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
|Keywords||Direct discrimination Indirect discrimination Affirmative actions Classification rules Data mining Knowledge discovery Logic programming|
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References found in this work BETA
Andrew Stranieri, John Zeleznikow, Mark Gawler & Bryn Lewis (1999). A Hybrid Rule – Neural Approach for the Automation of Legal Reasoning in the Discretionary Domain of Family Law in Australia. Artificial Intelligence and Law 7 (2-3):153-183.
John Zeleznikow, George Vossos & Daniel Hunter (1993). The IKBALS Project: Multi-Modal Reasoning in Legal Knowledge Based Systems. [REVIEW] Artificial Intelligence and Law 2 (3):169-203.
Citations of this work BETA
Bettina Berendt & Sören Preibusch (2014). Better Decision Support Through Exploratory Discrimination-Aware Data Mining: Foundations and Empirical Evidence. Artificial Intelligence and Law 22 (2):175-209.
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