The DOI matches its Crossref record; 442 of the 442 references it cites were found in a registry or an index.
A multidisciplinary survey on discrimination analysis
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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In the citing paper
“the machine learning field has seen an explosion of research on fairness.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Romei and Ruggieri (2014)
In the citing paper
“This literature provides discussions and debates on how to define fairness of predictive models and how to measure the performance in terms of discrimination.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Romei and Ruggieri (2014)
In the citing paper
“Not considering correlated variables has been shown to increase the risk of discrimination (e.g., redlining 4 ) [54,88,89,89,196,202,231,247,286,308].”
From Fairness in machine learning: A survey (2024) · cites this paper as [247]
In the citing paper
“There is also the risk of redlining, where although the sensitive attribute is łhandledž suiciently, correlated variables are still present [54,89,231,247,286,308], amplifying instead of reducing unfairness [89].”
From Fairness in machine learning: A survey (2024) · cites this paper as [247]
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“Barocas and Selbst survey a range of ways in which data-analysis algorithms can lead to discriminatory outcomes [3], and review articles by Romei and Ruggieri [25] and Zliobaite [30] survey data-analytic and algorithmic methods for measuring discrimination.”
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Metadata
- Authors
- Romei, A., & Ruggieri, S.
- Affiliations
- University of Pisa (IT)
- Venue
- The Knowledge Engineering Review
- Publisher
- Maximum Academic Press
- Published
- 2013-04-03
- Volume
- 29
- Issue
- 5
- Pages
- 582-638
- ISSN
- 0269-8889 (ISSN-L)
- 1469-8005
- OpenAlex
- W2166454173
- Semantic Scholar
- b0d3968e7a630d3661cd0878acc38c85f1828a9c
- Cited by
- 288
- References
- 442
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 21 · 2023: 31 · 2022: 27 · 2021: 33 · 2020: 23 · 2019: 38 · 2018: 31 · 2017: 28 · 2016: 17 · 2015: 9 · 2014: 5 · 2013: 3
- FWCI
- 29.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Knowledge Engineering Review (publishedVersion)
- UnipiEprints Open Archive (Università di Pisa) (acceptedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- ISTI Open Portal (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- discrimination analysis, anti-discrimination, observational studies, quasi-experimental design, experimental study, knowledge discovery, labor economics, social profiling, consumer market
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