{"type":"ScholarlyArticle","title":"Bias On Demand: Investigating Bias with a Synthetic Data Generator","authors":[{"name":"Joachim Baumann","slug":"joachim-baumann","given":"Joachim","family":"Baumann","affiliation":"Department of Informatics , University of Zurich , Switzerland; Institute of Data Analysis and Process Design , Zurich University of Applied Sciences , Switzerland"},{"name":"Alessandro Castelnovo","slug":"alessandro-castelnovo","given":"Alessandro","family":"Castelnovo","affiliation":"Data Science and Artificial Intelligence , Intesa Sanpaolo S.p.A. , Italy; Dept. of Informatics, Systems and Communication , Univ. of Milan-Bicocca , Italy"},{"name":"Andrea Cosentini","slug":"andrea-cosentini","given":"Andrea","family":"Cosentini","affiliation":"Data Science and Artificial Intelligence , Intesa Sanpaolo S.p.A. , Italy"},{"name":"Riccardo Crupi","slug":"riccardo-crupi","given":"Riccardo","family":"Crupi","affiliation":"Data Science and Artificial Intelligence , Intesa Sanpaolo S.p.A. , Italy"},{"name":"Nicole Inverardi","slug":"nicole-inverardi","given":"Nicole","family":"Inverardi","affiliation":"Data Science and Artificial Intelligence , Intesa Sanpaolo S.p.A. , Italy"},{"name":"Daniele Regoli","slug":"daniele-regoli","given":"Daniele","family":"Regoli","affiliation":"Data Science and Artificial Intelligence , Intesa Sanpaolo S.p.A. , Italy"}],"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.","doi":"10.24963/ijcai.2023/828","url":"https://ijcai.org/proceedings/2023/828","pdf":"https://ijcai.org/proceedings/2023/0828.pdf","markdown":"https://ijcai.org/proceedings/2023/828.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/828","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Demo Track","track":"Demo Track","number":828,"pages":{"first":7110,"last":7114},"keywords":["AI Ethics, Trust, Fairness: ETF: Bias","AI Ethics, Trust, Fairness: ETF: Trustworthy AI"],"videos":[],"references":[{"position":1,"raw":"[Angwin et al., 2016] Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 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