A survey on datasets for fairness‐aware machine learning

Tai Le Quy · Arjun G. Roy · Vasileios Iosifidis

261 citations0/0 files storedWiley Interdisciplinary Reviews Data Mining and Knowledge Discovery

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Ethics and Social Impacts of AI Privacy-Preserving Technologies in Data Explainable Artificial Intelligence (XAI)

Abstract

Abstract 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

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 261 citations exist upstream.

References

2011Thinking, Fast and Slow 2021A Survey on Bias and Fairness in Machine Learning 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2012Fairness through awareness 2016Equality of Opportunity in Supervised Learning 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2015Certifying and Removing Disparate Impact 2018Mitigating Unwanted Biases with Adversarial Learning

Cited by

2023Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey 2022Algorithmic fairness datasets: the story so far 2024Policy advice and best practices on bias and fairness in AI 2024A Rank Graduation Box for SAFE AI 2023Fairness in Deep Learning: A Survey on Vision and Language Research 2025Exploring the Intersection of Machine Learning and Big Data: A Survey

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.