Discrimination-aware data mining
Abstract
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. Rules extracted from databases by data mining techniques, such as classification or association rules, when used for decision tasks such as benefit or credit approval, can be discriminatory in the above sense. In this paper, the notion of discriminatory classification rules is introduced and studied. Providing a guarantee of non-discrimination is shown to be a non trivial task. A naive approach, like taking away all discriminatory attributes, is shown to be not enough when other background knowledge is available. Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge. An empirical assessment of the results on the German credit dataset is also provided.
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Techniques in discrimination-aware data mining attempt to define fairness and statistically assess it, assure it, or get closer to it.
“Techniques in discrimination-aware data mining attempt to define fairness (primarily in reference to anti-discrimination law [29]) and statistically assess it, assure it, or get closer to it [64, 32, 23].”
From Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018) · cites this paper as [64]
Post-algorithmic bias emerges when users interpret the output of machine learning algorithms.
“The post-algorithmic bias emerges when users interpret the output of machine learning algorithms [97–100].”
From Evolution and impact of bias in human and machine learning algorithm interaction (2020) · cites this paper as [98]
Not considering correlated variables has been shown to increase the risk of discrimination.
“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 [231]
Discrimination may not be one variable in isolation, but rather the result of several joint characteristics.
“It also ignores that discrimination may not be one variable in isolation, but rather the result of several joint characteristics [231].”
From Fairness in machine learning: A survey (2024) · cites this paper as [231]
There is a risk of redlining, where although the sensitive attribute is 'handled' sufficiently, correlated variables are still present, amplifying instead of reducing unfairness.
“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 [231]
A biased machine learning model that produces ranked lists can further systematically reduce the visibility of an already disadvantaged group.
“The main concern motivating this paper is that a biased machine learning model that produces ranked lists can further systematically reduce the visibility of an already disadvantaged group [10, 31]”
From FA*IR: A Fair Top-k Ranking Algorithm (2017) · cites this paper as [31]
Algorithms that constrain disparities in treatment and impact could negatively affect females with short hair and/or programming skills because of those features' association with males.
“For instance, the algorithms that put constraints on the aforementioned disparities in treatment and impact [17, 41, 61] could negatively affect females with short hair and/or programming skills, because of those features' (fair or unfair) association with males [32].”
From Marrying Fairness and Explainability in Supervised Learning (2022) · cites this paper as [41]
Several methods have been proposed to train machine learning models that prevent a combination of disparate treatment and impact.
“Several methods have been proposed to train machine learning models that prevent a combination of disparate treatment and impact [17, 41, 61].”
From Marrying Fairness and Explainability in Supervised Learning (2022) · cites this paper as [41]
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- Authors
- Pedreshi, D., Ruggieri, S., & Turini, F.
- Affiliations
- Università di Pisa, Pisa, Italy
- University of Pisa (IT)
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 560-568
- OpenAlex
- W2026019770
- Cited by
- 676
- References
- 0
- Citations by year
- 2026: 8 · 2025: 29 · 2024: 51 · 2023: 107 · 2022: 63 · 2021: 68 · 2020: 68 · 2019: 72 · 2018: 45 · 2017: 38 · 2016: 32 · 2015: 16 · 2014: 25 · 2013: 10 · 2012: 20
- FWCI
- 41.49
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- UnipiEprints Open Archive (Università di Pisa) (acceptedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- ISTI Open Portal (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic discrimination, redlining, civil rights legislation, prior knowledge, credit approval, German credit dataset, algorithmic fairness, association rule mining
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