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2014 Volume 29
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RESEARCH ARTICLE   Open Access    

A multidisciplinary survey on discrimination analysis

  • Dipartimento di Informatica

More Information
  • Abstract: The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.
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  • Cite this article

    Andrea Romei, Salvatore Ruggieri. 2014. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review 29(5)582−638, doi: 10.1017/S0269888913000039
    Andrea Romei, Salvatore Ruggieri. 2014. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review 29(5)582−638, doi: 10.1017/S0269888913000039
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RESEARCH ARTICLE   Open Access    

A multidisciplinary survey on discrimination analysis

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  • Published online: 03 April 2013
The Knowledge Engineering Review  29,  2014, 29(5): 582−638  |  Cite this article

Abstract: Abstract: The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.

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    Acknowledgments

    • The authors are grateful to Franco Turini and to the anonymous reviewers for several helpful comments.

    • Sex refers to a person's biological status, categorized as male, female, or intersex; gender refers to the attitudes and behaviors that a culture associates with a person's sex, categorized as masculine, feminine and transgender; sexual orientation refers to the sex of those to whom one is sexually and romantically attracted, categorized as homosexual, heterosexual, and bisexual. See American Psychological Association (2011) for a psychological discussion of the differences between the terms, and Case (1995) for a discussion with reference to the US anti-discrimination law.

    • Race is a social construct to categorize people into groups. The term is controversial, and with little consensus on its actual meaning. Blank et al. (2004: Ch. 2) summarize biological and social concepts of race, and discuss US categorizations of races used for data collection, for example, in census data. Ethnicity refers to self-identifying groups based on beliefs concerning shared culture, ancestry and history.

    • See also http://www.understandingprejudice.org for a collection of prejudice-related resources.

    • While semantically equivalent, the term ‘non-discrimination law’ recalls a set of negative obligations, while ‘equality law’ recalls, in addition, a set of positive obligations to reach the ideal of equal treatment. See Bell (2002).

    • See also the European Network of Legal Experts (http://www.non-discrimination.net), the EU Agency for Fundamental Rights (http://fra.europa.eu), and the Migration Policy Group (http://www.migpolgroup.com).

    • Prima facie is a Latin term meaning ‘at first look’, or ‘on its face’, and refers to evidence, which, unless rebutted, would be sufficient to prove a particular proposition or fact.

    • A recurring problem known as the omitted-variable bias.

    • Aggregated data taken from the US Current Population Survey, available at http://www.bls.gov/cps

    • See, for example, Koenker (2005).

    • The marginal product is the extra output produced by using one more worker.

    • The standard score of a value x for a response is $$\[--><$>(x{\rm{ - }}\mu )/\sigma <$><!--$$, where μ is the mean and σ is the standard deviation of the response over the sample. To account for questions not appearing in all years, authors deviate slightly by taking μ as the mean for the year 1977, which contains almost all questions, and σ is the standard deviation for the first year the question appeared in the GSS data.

    • http://www.aeaweb.org/econlit

    • http://www.ipums.org

    • http://laborsta.ilo.org

    • http://ciri.binghamton.edu

    • Bonoli and Hinrichs (2010) argue that the hiring channel, that is, the way job applicants contact employers (e.g. through a friend or a relative), which is mostly considered an indicator of motivation, actually plays a stronger role than age or gender as a signal of productivity.

    • See e.g. Christensen (2002).

    • See e.g. Greene (2008).

    • Acronym of Geweke–Hajivassiliou–Keane, see Keane (1994).

    • The statistical basis of propensity score weighting can be found in Rosenbaum and Rubin (1983).

    • See Hoeting et al. (1999) for an introduction.

    • http://powerreporting.com/color

    • http://www.yellowwindow.be/genderinresearch

    • http://www.portal.advance.vt.edu

    • See Goldstein (2011)

    • https://implicit.harvard.edu/implicit

    • Bendick (2007) reports a single ‘outlier’ study, whose methodology and rigor have been seriously questioned, which found a net rate of −10%, denoting a better treatment of the Hispanic testers compared to their counterparts.

    • The statistical basis of the approach can be found in Ghosh et al. (2000).

    • See Siegel and Castellan (1988).

    • Where automated systems are designed to be ‘capable of reproducing the judgment of the recruitment consultant’ (Kessler et al., 2009).

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    Andrea Romei, Salvatore Ruggieri. 2014. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review 29(5)582−638, doi: 10.1017/S0269888913000039
    Andrea Romei, Salvatore Ruggieri. 2014. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review 29(5)582−638, doi: 10.1017/S0269888913000039
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