Bias on demand : investigating bias with a synthetic data generator

Baumann, Joachim; Castelnovo, Alessandro; Cosentini, Andrea; Crupi, Riccardo; Inverardi, Nicole; Regoli, Daniele

doi:10.24963/ijcai.2023/828

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Bias on demand : investigating bias with a synthetic data generator
Authors:	Baumann, Joachim Castelnovo, Alessandro Cosentini, Andrea Crupi, Riccardo Inverardi, Nicole Regoli, Daniele
et. al:	No
DOI:	10.24963/ijcai.2023/828
Proceedings:	Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Page(s):	7110
Pages to:	7114
Conference details:	32nd International Joint Conference on Artificial Intelligence (IJCAI), Macao, S.A.R, 19-25 August 2023
Issue Date:	Aug-2023
Publisher / Ed. Institution:	International Joint Conferences on Artificial Intelligence Organization
ISBN:	978-1-956792-03-4
Language:	English
Subjects:	Digitalisierung; Machine learning; Bias; Vorurteil
Subject (DDC):	006: Special computer methods
Abstract:	Machine Learning (ML) systems are increasingly being adopted to make decisions that might have a significant impact on people’s lives. Because these decision-making systems rely on data-driven learning, the risk is that they will systematically propagate the bias embedded in the data. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced and possibly how to mitigate it. We demonstrate Bias on Demand, a framework to generate synthetic datasets with different types of bias, which is available as an open-source toolkit and as a pip package. We include a demo of our proposed synthetic data generator, in which we illustrate experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. We encourage readers to explore the full paper for a more detailed analysis.
URI:	https://digitalcollection.zhaw.ch/handle/11475/29508
Fulltext version:	Published version
License (according to publishing contract):	Not specified
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Published as part of the ZHAW project:	Socially acceptable AI and fairness trade-offs in predictive analytics Algorithmic Fairness in data-based decision making: Combining ethics and technology
Appears in collections:	Publikationen School of Engineering

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Baumann, J., Castelnovo, A., Cosentini, A., Crupi, R., Inverardi, N., & Regoli, D. (2023). Bias on demand : investigating bias with a synthetic data generator [Conference paper]. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 7110–7114. https://doi.org/10.24963/ijcai.2023/828

Baumann, J. et al. (2023) ‘Bias on demand : investigating bias with a synthetic data generator’, in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, pp. 7110–7114. Available at: https://doi.org/10.24963/ijcai.2023/828.

J. Baumann, A. Castelnovo, A. Cosentini, R. Crupi, N. Inverardi, and D. Regoli, “Bias on demand : investigating bias with a synthetic data generator,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Aug. 2023, pp. 7110–7114. doi: 10.24963/ijcai.2023/828.

BAUMANN, Joachim, Alessandro CASTELNOVO, Andrea COSENTINI, Riccardo CRUPI, Nicole INVERARDI und Daniele REGOLI, 2023. Bias on demand : investigating bias with a synthetic data generator. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. Conference paper. International Joint Conferences on Artificial Intelligence Organization. August 2023. S. 7110–7114. ISBN 978-1-956792-03-4

Baumann, Joachim, Alessandro Castelnovo, Andrea Cosentini, Riccardo Crupi, Nicole Inverardi, and Daniele Regoli. 2023. “Bias on Demand : Investigating Bias with a Synthetic Data Generator.” Conference paper. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 7110–14. International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2023/828.

Baumann, Joachim, et al. “Bias on Demand : Investigating Bias with a Synthetic Data Generator.” Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, 2023, pp. 7110–14, https://doi.org/10.24963/ijcai.2023/828.