A survey on datasets for fairness‐aware machine learning

By Tai Le Quy, Arjun Roy, Vasileios Iosifidis, Eirini Ntoutsi. WIREs Data Mining Knowl. Discov., 2021

354 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Safety Research

View Paper: https://www.semanticscholar.org/paper/ebb0df8229e7dcf47f79c96e77b37e6fa3dae485

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Christian Haas
2. Waldemar Karwowski
3. Ben Shneiderman
4. Hernisa Kacorri
5. Marina Jirotka
6. Wei Xu
7. Simon Caton
8. J. Konstan

In Safety Research

1. Ben Shneiderman
2. Christian Haas
3. Marina Jirotka
4. Wei Xu
5. Simon Caton

Citations in the corpus

• Six Human-Centered Artificial Intelligence Grand Challenges (2023, background): “Lastly, because AI and ML algorithms often rely on large amounts of data, availability of fairness-aware datasets to train these algorithms and benchmark datasets to test to ensure that AI systems produce fair and equitable outputs is important (Quy et al., 2021).”
• Fairness in Machine Learning: A Survey (2020, methodology): “Synopsis:Within the sampling approaches, the application of decoupled classiiers and multitask learning has emerged (see [9, 52, 90, 225, 283, 307]).”