Articolo in rivista, 2010, ENG, 10.1007/s10506-010-9089-5

Integrating induction and deduction for finding evidence of discrimination

Pedreschi D.; Turini F.; Ruggieri S.

Dipartimento di matematica, Universita' di Pisa, Pisa, Italy - CNR-ISTI, Pisa, Italy; Dipartimento di matematica, Universita' di Pisa, Pisa, Italy - CNR-ISTI, Pisa, Italy; Dipartimento di matematica, Universita' di Pisa, Pisa, Italy - CNR-ISTI, Pisa, Italy

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.

Artificial intelligence and law (Dordr., Online) 18 (1), pp. 1–43

Keywords

Direct discrimination, Indirect discrimination, Classification rules-Data mining, Knowledge discovery, Logic programming, Affirmative actions

CNR authors

Ruggieri Salvatore, Turini Franco, Pedreschi Dino

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 68520

Year: 2010

Type: Articolo in rivista

Creation: 2012-02-26 00:00:00.000

Last update: 2018-02-01 17:05:27.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:68520

DOI: 10.1007/s10506-010-9089-5