Statistical Reasoning in the Legal Setting

Pedreschi

ACM Trans. Knowl. Discov. Data

2010

133

119

In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Discrimination in credit, mortgage, insurance, labor market, and education has been investigated by researchers in economics and human sciences. With the advent of automatic decision support systems, such as credit scoring systems, the ease of data collection opens several challenges to data analysts for the fight against discrimination. In this paper, we introduce the problem of discovering discrimination through data mining in a dataset of historical decision records, taken by humans or by automatic systems. We formalize the processes of direct and indirect discrimination discovery by modelling protected-by-law groups and contexts where discrimination occurs in a classification rule based syntax. Basically, classification rules extracted from the dataset allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-bylaw groups is formalized by an extension of the lift measure of a classification rule. In direct discrimination, the extracted rules can be directly mined in search of discriminatory contexts. In indirect discrimination, the mining process needs some background knowledge as a further input, e.g., census data, that combined with the extracted rules might allow for unveiling contexts of discriminatory decisions. A strategy adopted for combining extracted classification rules with background knowledge is called an inference model. In this paper, we propose two inference models and provide automatic procedures for their implementation. An empirical assessment of our results is provided on the German credit dataset and on the PKDD Discovery Challenge 1999 financial dataset.

Section: Discrimination Discovery and Discrimination Preventionmentioning

confidence: 99%

Data mining for discrimination discovery

Pedreschi

ACM Trans. Knowl. Discov. Data

2010

133

119

“…Statistical significance of the diff () measure should also be taken into account, and it is actually customary in legal cases [13,16]. In our context, we can interpret the observed proportions p1 and p2 from Def.…”

Section: K-nn As Situation Testingmentioning

confidence: 99%

k-NN as an implementation of situation testing for discrimination discovery and prevention

Luong

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

2011

183

144

With the support of the legally-grounded methodology of situation testing, we tackle the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification. A tuple is labeled as discriminated if we can observe a significant difference of treatment among its neighbors belonging to a protected-by-law group and its neighbors not belonging to it. Discrimination discovery boils down to extracting a classification model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classifier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals.

“…Figure 1: Contingency table for a classification rule employment discrimination literature [8], the "event" modelled is promotion or hiring of a person. The odds of a classification rule A, B → C can then be defined as:…”

Section: Ratio Measuresmentioning

confidence: 99%

Measuring Discrimination in Socially-Sensitive Decision Records

Pedreschi

Proceedings of the 2009 SIAM International Conference on Data Mining

2009

156

144

Discrimination in social sense (e.g., against minorities and disadvantaged groups) is the subject of many laws worldwide, and it has been extensively studied in the social and economic sciences. We tackle the problem of determining, given a dataset of historical decision records, a precise measure of the degree of discrimination suffered by a given group (e.g., an etnic minority) in a given context (e.g., a geographic area) with respect to the decision (e.g. credit denial). In our approach, this problem is rephrased in a classification rule based setting, and a collection of quantitative measures of discrimination is introduced, on the basis of existing norms and regulations. 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. Based on this basic method, we are then able to address the more general problems of: (1) unveiling all discriminatory decision patterns hidden in the historical data, combining discrimination analysis with association rule mining, (2) unveiling discrimination in classifiers that learn over training data biased by discriminatory decisions, and (3) in the case of rule-based classifiers, sanitizing discriminatory rules by correcting their confidence. Our approach is validated on the German credit dataset and on the CPAR classifier.