Handling Conditional Discrimination

liobaite, Indre; Kamiran, Faisal; Calders, Toon

doi:10.1109/icdm.2011.72

Cited by 104 publications

(142 citation statements)

References 9 publications

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“…Zliobaite et al [45] focus on the concept of genuine requirement to detect that part of discrimination which may be explained by other, legally grounded, attributes. In [7], Dwork et al address the problem of fair classification that achieves both group fairness, i.e., the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole, and individual fairness, i.e., similar individuals should be treated similarly.…”

Section: Related Workmentioning

confidence: 99%

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Exposing the probabilistic causal structure of discrimination

Bonchi¹,

Hajian²,

Mishra³

et al. 2017

Int J Data Sci Anal

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Discrimination discovery from data is an important data mining task, whose goal is to identify patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities. While any legally valid proof of discrimination requires evidence of causality, the state-of-the-art methods are essentially correlation based, albeit, as it is well known, correlation does not imply causation. In this paper, we take a principled causal approach to discrimination detection following Suppes' probabilistic causation theory. In particular, we define a method to extract, from a dataset of historical decision records, the causal structures existing among the attributes in the data. The result is a type of constrained Bayesian network, which we dub Suppes-Bayes causal network (SBCN). Next, we develop a toolkit of methods based on random walks on top of the SBCN, addressing different anti-discrimination legal concepts, such as direct and indirect discrimination, Milano-Bicocca University, Milan, Italy group and individual discrimination, genuine requirement, and favoritism. Our experiments on real-world datasets confirm the inferential power of our approach in all these different tasks.

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Section: Related Workmentioning

confidence: 99%

“…In the state of the art of data mining methods for discrimination discovery, it is also known as explainable discrimination [12] and conditional discrimination [45].…”

Section: Genuine Requirementmentioning

confidence: 99%

Exposing the probabilistic causal structure of discrimination

Bonchi¹,

Hajian²,

Mishra³

et al. 2017

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…When there are significantly different decision outcomes between the testers of the protected group and the testers of the non-protected group, the negative decision can be considered as discrimination. Conditional discrimination, i.e., part of discrimination may be explained by other legally grounded attributes, was studied in [43]. The task was to evaluate to which extent the discrimination apparent for a group is explainable on a legal ground.…”

Section: Discrimination Discoverymentioning

confidence: 99%

“…The state of the art of discrimination discovery has developed different approaches for discovering discrimination [20,21,26,27,29,30,43]. These approaches classify discrimination into different types such as group discrimination, individual discrimination, direct and indirect discrimination.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches classify discrimination into different types such as group discrimination, individual discrimination, direct and indirect discrimination. Based on that, methods for discrimination prevention have been proposed [1,7,9,[12][13][14][15][17][18][19]21,35,37,43] which either use data preprocessing or algorithm tweaking. However, these works are mainly based on correlation or association-based measures which cannot be used to estimate the causal effect of the protected attributes on the decision.…”

Section: Introductionmentioning

confidence: 99%

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Anti-discrimination learning: a causal modeling-based framework

Zhang

2017

Int J Data Sci Anal

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Anti-discrimination learning is an increasingly important task in data mining. Discrimination discovery is the problem of unveiling discriminatory practices by analyzing a dataset of historical decision records, and discrimination prevention aims to remove discrimination by modifying the biased data and/or the predictive algorithms. Discrimination is causal, which means that to prove discrimination one needs to derive a causal relationship rather than an association relationship. Although it is well known that association does not mean causation, the gap between association and causation is not paid enough attention by many researchers. In this paper, we introduce a causal modeling-based framework for anti-discrimination learning. Discrimination is categorized according to two dimensions: direct/indirect and system/group/individual level. Within the causal framework, we introduce a work for discovering and preventing both direct and indirect system-level discrimination in the training data, and a work for extending the non-discrimination result from the training data to prediction. We then introduce two works for group-level direct discrimination and individual-level direct discrimination respectively. The aim of this paper is to deepen the understanding of discrimination in data mining from the causal modeling perspective, and suggest several potential future research directions.

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A survey on datasets for fairness‐aware machine learning

Quy

Roy

Iosifidis

et al. 2022

WIREs Data Min & Knowl

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As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue of fairness in data‐driven artificial intelligence systems is receiving increasing attention from both research and industry. A large variety of fairness‐aware ML solutions have been proposed which involve fairness‐related interventions in the data, learning algorithms, and/or model outputs. However, a vital part of proposing new approaches is evaluating them empirically on benchmark datasets that represent realistic and diverse settings. Therefore, in this paper, we overview real‐world datasets used for fairness‐aware ML. We focus on tabular data as the most common data representation for fairness‐aware ML. We start our analysis by identifying relationships between the different attributes, particularly with respect to protected attributes and class attribute, using a Bayesian network. For a deeper understanding of bias in the datasets, we investigate interesting relationships using exploratory analysis. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Fundamental Concepts of Data and Knowledge > Data Concepts Technologies > Data Preprocessing

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Handling Conditional Discrimination

Cited by 104 publications

References 9 publications

Exposing the probabilistic causal structure of discrimination

Exposing the probabilistic causal structure of discrimination

Anti-discrimination learning: a causal modeling-based framework

A survey on datasets for fairness‐aware machine learning

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