About | HeinOnline Law Journal Library | HeinOnline Law Journal Library | HeinOnline
LOG IN
LOG IN

18 A.I. & L. 1 (2010)

handle is hein.journals/artinl18 and id is 1 raw text is: Artif Intell Law (2010) 18:1-43 DOI 10.1007/s10506-010-9089-5 Integrating induction and deduction for finding evidence of discrimination Salvatore Ruggieri - Dino Pedreschi - Franco Turini Published online: 5 June 2010 © Springer Science+Business Media B.V. 2010 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 disad- vantaged 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 eval- uated by formalizing existing norms and regulations in terms of quantitative mea- sures. 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 rea- sonings 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 implementa- tion, called LP2DD, of the overall reference model that integrates induction, through data mining classification rule extraction, and deduction, through a com- putational logic implementation of the analytical tools. The LP2DD system is put at work on the analysis of a dataset of credit decision records. S. Ruggieri (E) - D. Pedreschi - F. Turini Dipartimento di Informatica, Universiti di Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy e-mail: ruggieri@di.unipi.it D. Pedreschi e-mail: pedre@di.unipi.it F. Turini e-mail: turini@di.unipi.it I Springer

What Is HeinOnline?

HeinOnline is a subscription-based resource containing thousands of academic and legal journals from inception; complete coverage of government documents such as U.S. Statutes at Large, U.S. Code, Federal Register, Code of Federal Regulations, U.S. Reports, and much more. Documents are image-based, fully searchable PDFs with the authority of print combined with the accessibility of a user-friendly and powerful database. For more information, request a quote or trial for your organization below.



Short-term subscription options include 24 hours, 48 hours, or 1 week to HeinOnline.

Purchase Access

Contact us for annual subscription options:

Contact Us

Already a HeinOnline Subscriber?

Log In

profiles profiles most