Bias On Demand: Investigating Bias with a Synthetic Data Generator
Bias On Demand: Investigating Bias with a Synthetic Data Generator
Joachim Baumann, Alessandro Castelnovo, Andrea Cosentini, Riccardo Crupi, Nicole Inverardi, Daniele Regoli
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Demo Track. Pages 7110-7114.
https://doi.org/10.24963/ijcai.2023/828
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.
Keywords:
AI Ethics, Trust, Fairness: ETF: Bias
AI Ethics, Trust, Fairness: ETF: Trustworthy AI