On Proving Discrimination: Statistical Methods and Unfolding Policy Logics

Knopff, Rainer

doi:10.2307/3550667

Cited by 13 publications

(8 citation statements)

References 3 publications

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“…This relied on the circumstantial evidence provided by the unequal outcomes of the recruitment process. As in other jurisdictions, anti-discrimination legislation reduces the incidence of overt discrimination (or at least makes it more difficult to prove), and as a result, courts have relied increasingly on circumstantial evidence, such as the differences between applicant and appointee populations (Knopff, 1986). In undisputed evidence, the Tribunal heard that after considering 750 applicants for cabin crew employment in the period September 2001 to September 2002, Virgin Blue had employed only one person who was aged over 35 years.…”

Section: The Legal Case Of Hopper and Others Vs Virgin Bluementioning

confidence: 99%

Discrimination, labour markets and the labour market prospects of older workers: what can a legal case teach us?

Weller

2007

Work, Employment and Society

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As governments become increasingly concerned about the fiscal implications of the ageing population, labour market policies have sought to encourage mature workers to remain in the labour force. The 'human capital' discourses motivating these policies rest on the assumption that older workers armed with motivation and vocational skills will be able to return to fulfilling work. This paper uses the post-redundancy recruitment experiences of former Ansett Airlines flight attendants to develop a critique of these expectations. It suggests that policies to increase older workers' labour market participation will not succeed while persistent socially constructed age-and gender-typing shape labour demand. The conclusion argues for policies sensitive to the institutional structures that shape employer preferences, the competitive rationality of discriminatory practices, and the irresolvable tension between workers' human rights and employers' property rights. KEY WORDSage / discrimination / recruitment / older workers / flight attendants

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Section: The Legal Case Of Hopper and Others Vs Virgin Bluementioning

confidence: 99%

Discrimination, labour markets and the labour market prospects of older workers: what can a legal case teach us?

Weller

2007

Work, Employment and Society

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“…Legislation 2009;Australian Legislation 2009;Hunter 1992;Knopff 1986]. Direct discrimination consists of rules or procedures that explicitly impose "disproportionate burdens" on minority or disadvantaged groups.…”

Section: Modelling the Process Of Direct Discrimination Analysismentioning

confidence: 99%

“…A general principle in discrimination laws is to consider group representation [Knopff 1986] as a quantitative measure of the qualitative requirement that people in a group are treated "less favorably" [European Union Legislation 2009; U.K. Legislation 2009] than others, or such that "a higher proportion of people without the attribute comply or are able to comply" [Australian Legislation 2009] to a qualifying criteria. We observe that (see Lemma A.9):…”

Section: α-Protectionmentioning

confidence: 99%

Data mining for discrimination discovery

Ruggieri

Pedreschi

Turini

2010

ACM Trans. Knowl. Discov. Data

133

119

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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.

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“…The ratio of the two frequencies, namely 2, will be later on defined as a measure of discrimination. If a ratio of 2 would be deemed unacceptable by the law, and the provided input pool would be representative of the underlying population [14], we could conclude that the DSS decisions have discriminatory effects for women living in the area ZIP = 101. Although the DSS logic has no explicit discriminatory intent, its analyses are not complete enough to prevent what is known in the literature as indirect or systematic discrimination.…”

Section: Dss As a Black-boxmentioning

confidence: 99%

“…Also, we [14] as a quantitative measure of the qualitative requirement that people in a group are treated "less favorably" [8,28] than others, or such that "a higher proportion of people without the attribute comply or are able to comply" [2] to a qualifying criteria. We recall from [19] the notion of extended lift, a measure of the increased confidence in concluding an assertion C resulting from adding (potentially discriminatory) information A to a rule B → C where no PD itemset appears.…”

Section: Measures Of Discriminationmentioning

confidence: 99%

Integrating induction and deduction for finding evidence of discrimination

2010

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Automatic Decision Support Systems (DSS) are widely adopted for screening purposes in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. While less arbitrary decisions can potentially be guaranteed, automatic DSS can still be discriminating in the socially negative sense of resulting in unfair or unequal treatment of people. We present a reference model for finding (prima facie) evidence of discrimination in automatic DSS which is driven by a few key legal concepts. First, frequent classification rules are extracted from the set of decisions taken by the DSS over an input pool dataset. Key legal concepts are then used to drive the analysis of the set of classification rules, with the aim of discovering patterns of discrimination. We present an implementation, called LP2DD, of the overall reference model integrating induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools.

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On Proving Discrimination: Statistical Methods and Unfolding Policy Logics

Cited by 13 publications

References 3 publications

Discrimination, labour markets and the labour market prospects of older workers: what can a legal case teach us?

Discrimination, labour markets and the labour market prospects of older workers: what can a legal case teach us?

Data mining for discrimination discovery

Integrating induction and deduction for finding evidence of discrimination

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