Discrimination-aware data mining: a survey

Kashid, Asmita; Kulkarni, Vrushali; Patankar, Ruhi

doi:10.1504/ijds.2017.082748

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…The ultimate goal of such a process is to make pattern mining more practically useful by making the end user understand during the mining process how mining results come to pass. Discrimination-aware data mining exists for more than a decade now [26,19]. It mainly focuses on developing methods for protecting from unfair classification models, especially when they might affect somebody's life.…”

Section: Related Workmentioning

confidence: 99%

Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL Data

Koptelov

Zimmermann

Boizumault

et al. 2023

Lecture Notes in Computer Science

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In sensitive applications, such as drug development, offering experts an explanation for why data mining operations arrive at certain results adds a very valuable facet. In this work we benefit from modelling the task as a Constraint Satisfaction Problem (CSP) twice: by adding multiple constraints to the mining process and by deriving pattern failure explanations. We illustrate experimentally how to apply our method on data originally retrieved from the ChEMBL database [14]. We also report some interesting dependencies discovered by our method which are not easy to observe when analysing data manually.

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Section: Related Workmentioning

confidence: 99%

Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL Data

Koptelov

Zimmermann

Boizumault

et al. 2023

Lecture Notes in Computer Science

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show abstract

“…Hence, it is possible to achieve stable and fair classifiers by designing algorithms that satisfy differential privacy and fairness simultaneously. Recent studies [37,38,44,45,66] have expanded the application of methods to achieve both goals; see a recent paper [25] for more discussions. However, these methods are almost all heuristic and without theoretical guarantee.…”

Section: Other Related Workmentioning

confidence: 99%

Stable and Fair Classification

Huang,

Vishnoi

2019

Preprint

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Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task. However, in a recent study, Friedler et al. observed that fair classification algorithms may not be stable with respect to variations in the training dataset -a crucial consideration in several real-world applications. Motivated by their work, we study the problem of designing classification algorithms that are both fair and stable. We propose an extended framework based on fair classification algorithms that are formulated as optimization problems, by introducing a stability-focused regularization term. Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework. Our accuracy guarantee can be used to inform the selection of the regularization parameter in our framework. To the best of our knowledge, this is the first work that combines stability and fairness in automated decision-making tasks. We assess the benefits of our approach empirically by extending several fair classification algorithms that are shown to achieve the best balance between fairness and accuracy over the Adult dataset. Our empirical results show that our framework indeed improves the stability at only a slight sacrifice in accuracy.

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Image fairness in deep learning: problems, models, and challenges

Tian

Zhu

Liu

et al. 2022

Neural Comput & Applic

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In recent years, it has been revealed that machine learning models can produce discriminatory predictions. Hence, fairness protection has come to play a pivotal role in machine learning. In the past, most studies on fairness protection have used traditional machine learning methods to enforce fairness. However, these studies focus on low dimensional inputs, such as numerical inputs, whereas more recent deep learning technologies have encouraged fairness protection with image inputs through deep model methods. These approaches involve various object functions and structural designs that break the spurious correlations between targets and sensitive features. With these connections broken, we are left with fairer predictions. To better understand the proposed methods and encourage further development in the field, this paper summarizes fairness protection methods in terms of three aspects: the problem settings, the models, and the challenges. Through this survey, we hope to reveal research trends in the field, discover the fundamentals of enforcing fairness, and summarize the main challenges to producing fairer models.

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Discrimination-aware data mining: a survey

Cited by 5 publications

References 19 publications

Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL Data

Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL Data

Stable and Fair Classification

Image fairness in deep learning: problems, models, and challenges

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