TITLE: A survey on datasets for fairness-aware machine learning 

ABSTRACT: As decision-making increasingly relies on Machine Learning (ML) and (big)
data, the issue of fairness in data-driven Artificial Intelligence (AI) systems
is receiving increasing attention from both research and industry. A large
variety of fairness-aware machine learning 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 machine learning. We focus on tabular data as the most common
data representation for fairness-aware machine learning. We start our analysis
by identifying relationships between the different attributes, particularly
w.r.t. protected attributes and class attribute, using a Bayesian network. For
a deeper understanding of bias in the datasets, we investigate the interesting
relationships using exploratory analysis.
As decision-making increasingly relies on Machine Learning (ML) and (big)
data, the issue of fairness in data-driven Artificial Intelligence (AI) systems
is receiving increasing attention from both research and industry. A large
variety of fairness-aware machine learning 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 machine learning. We focus on tabular data as the most common
data representation for fairness-aware machine learning. We start our analysis
by identifying relationships between the different attributes, particularly
w.r.t. protected attributes and class attribute, using a Bayesian network. For
a deeper understanding of bias in the datasets, we investigate the interesting
relationships using exploratory analysis.
A survey on datasets for fairness-aware machine learning
http://arxiv.org/abs/2110.00530v3