Representation Bias in Data: A Survey on Identification and Resolution Techniques

Author:

Shahbazi Nima1ORCID,Lin Yin2ORCID,Asudeh Abolfazl1ORCID,Jagadish H. V.2ORCID

Affiliation:

1. University of Illinois Chicago, USA

2. University of Michigan, USA

Abstract

Data-driven algorithms are only as good as the data they work with, while datasets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons, ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. Given that “bias in, bias out,” one cannot expect AI-based solutions to have equitable outcomes for societal applications, without addressing issues such as representation bias. While there has been extensive study of fairness in machine learning models, including several review papers, bias in the data has been less studied. This article reviews the literature on identifying and resolving representation bias as a feature of a dataset, independent of how consumed later. The scope of this survey is bounded to structured (tabular) and unstructured (e.g., image, text, graph) data. It presents taxonomies to categorize the studied techniques based on multiple design dimensions and provides a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference124 articles.

1. 2019. Health United States Spotlight: Race and Ethnic Disparities in Heart Disease . Health United States spotlight CDC Stacks Public Health Publications. https://stacks.cdc.gov/view/cdc/77732.

2. 2019. Asian-American and Pacific Islander Heritage in the United States. https://www.census.gov/newsroom/facts-for-features/2019/asian-american-pacific-islander.html. Accessed 26-03-2023.

3. Active sampling for min-max fairness;Abernethy Jacob;arXiv preprint arXiv:2006.06879,2020

4. Adaptive sampling to reduce disparate performance;Abernethy Jacob;arXiv e-prints,2020

5. Chiara Accinelli, Barbara Catania, Giovanna Guerrini, and Simone Minisi. 2021. The impact of rewriting on coverage constraint satisfaction. In Proceedings of the EDBT/ICDT Workshops.

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