A survey on datasets for fairness‐aware machine learning

Quy, Tai Le; Roy, Arjun; Iosifidis, Vasileios; Zhang, Wenbin; Ntoutsi, Eirini

doi:10.1002/widm.1452

Cited by 81 publications

(44 citation statements)

References 95 publications

(113 reference statements)

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“…COMPAS 3 : This dataset describes the task of predicting the recidivism of individuals in the U.S. Both “sex” and “race” could be the sensitive attributes of this dataset (Le Quy et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Fair classification via domain adaptation: A dual adversarial learning approach

Liang

Chen²,

Tian³

et al. 2023

Front. Big Data

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Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stake applications. Recent research on fair classifiers has drawn significant attention to developing effective algorithms to achieve fairness and good classification performance. Despite the great success of these fairness-aware machine learning models, most of the existing models require sensitive attributes to pre-process the data, regularize the model learning or post-process the prediction to have fair predictions. However, sensitive attributes are often incomplete or even unavailable due to privacy, legal or regulation restrictions. Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes. Thus, it is important to exploit auxiliary information from a similar domain to help improve fair classification in the target domain. Therefore, in this paper, we study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can learn to adapt the sensitive attributes from a source domain for fair classification in the target domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model for fair classification, even when no sensitive attributes are available in the target domain.

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Section: Methodsmentioning

confidence: 99%

Fair classification via domain adaptation: A dual adversarial learning approach

Liang

Chen²,

Tian³

et al. 2023

Front. Big Data

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“…In this study, we focus on tabular datasets, mostly used in fairness-aware machine learning research (Le Quy et al, 2022). We use datasets from the financial (Adult Income dataset), criminological (Catalonia Juvenile dataset, Crimes and Communities dataset) and the educational (Student performance dataset, Law admission dataset) domain.…”

Section: Methodsmentioning

confidence: 99%

PreCoF: counterfactual explanations for fairness

2023

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This paper studies how counterfactual explanations can be used to assess the fairness of a model. Using machine learning for high-stakes decisions is a threat to fairness as these models can amplify bias present in the dataset, and there is no consensus on a universal metric to detect this. The appropriate metric and method to tackle the bias in a dataset will be case-dependent, and it requires insight into the nature of the bias first. We aim to provide this insight by integrating explainable AI (XAI) research with the fairness domain. More specifically, apart from being able to use (Predictive) Counterfactual Explanations to detect explicit bias when the model is directly using the sensitive attribute, we show that it can also be used to detect implicit bias when the model does not use the sensitive attribute directly but does use other correlated attributes leading to a substantial disadvantage for a protected group. We call this metric PreCoF , or Predictive Counterfactual Fairness. Our experimental results show that our metric succeeds in detecting occurrences of implicit bias in the model by assessing which attributes are more present in the explanations of the protected group compared to the unprotected group. These results could help policymakers decide on whether this discrimination is justified or not.

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“…Classification tasks on tabular data have been widely studied in the machine learning literature [23], [24]. One popular approach for such tasks is the use of decision tree-based algorithms, such as Random Forests [25] and XGBoost [26].…”

Section: Related Workmentioning

confidence: 99%

Discrimination Neural Network Model for Binary Classification Tasks on Tabular Data

Munkhdalai

Hong

et al. 2023

IEEE Access

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For the classification task, neural network-based approaches attempt to distinguish between two distributions by determining the joint distribution of input variables for each class. However, the most challenging task is still to classify the observations in the overlapping region of two classes. In this work, we propose a new discrimination neural network (DiscNN) architecture to address this issue. Our DiscNN learns to embed the initial input into more informative representations with better discriminability between the two distributions based on the cosine embedding loss. We also train our proposed model using the few-shot learning method to extract better-generalized representations from the initial input. We applied the DiscNN model to 35 tabular datasets from the OpenML-CC18 benchmark for a binary classification task. Our model showed superior performances on 28 datasets of them. In addition, we also performed experiments on 95 imbalanced datasets from the KEEL repository. The experiment results showed that the DiscNN outperformed the state-of-the-art models, including CatBoost, LightGBM, TabNet, VIME and Scarf, by around 0.23% AUC, 0.20% G-mean, and 1.06% F1 score.

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A survey on datasets for fairness‐aware machine learning

Cited by 81 publications

References 95 publications

Fair classification via domain adaptation: A dual adversarial learning approach

Fair classification via domain adaptation: A dual adversarial learning approach

PreCoF: counterfactual explanations for fairness

Discrimination Neural Network Model for Binary Classification Tasks on Tabular Data

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