A survey on bias in visual datasets. - Simone Fabbrizzi; Symeon Papadopoulos; Eirini Ntoutsi; Ioannis Kompatsiaris (Comput. Vis. Image Underst., 2022)

Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation. - Xuan Zhao; Simone Fabbrizzi; Paula Reyero Lobo; Siamak Ghodsi; Klaus Broelemann; Steffen Staab; Gjergji Kasneci (CoRR, 2023)

Adversarial Reweighting Guided by Wasserstein Distance to Achieve Demographic Parity. - Xuan Zhao; Simone Fabbrizzi; Paula Reyero Lobo; Siamak Ghodsi; Klaus Broelemann; Steffen Staab; Gjergji Kasneci (IEEE Big Data, 2024)

Policy Advice and Best Practices on Bias and Fairness in Artificial Intelligence. - José M. Álvarez 002; Alejandra Bringas Colmenarejo; Alaa Elobaid; Simone Fabbrizzi; Miriam Fahimi; Antonio Ferrara 003; Siamak Ghodsi; Carlos Mougan; Ioanna Papageorgiou; Paula Reyero Lobo; Mayra Russo; Kristen M. Scott; et al. (EWAF, 2024)

Studying bias in visual features through the lens of optimal transport. - Simone Fabbrizzi; Xuan Zhao; Emmanouil Krasanakis; Symeon Papadopoulos; Eirini Ntoutsi (Data Min. Knowl. Discov., 2024)

Correction to: Studying bias in visual features through the lens of optimal transport. - Simone Fabbrizzi; Xuan Zhao; Emmanouil Krasanakis; Symeon Papadopoulos; Eirini Ntoutsi (Data Min. Knowl. Discov., 2024)

Policy advice and best practices on bias and fairness in AI. - José M. Álvarez 002; Alejandra Bringas Colmenarejo; Alaa Elobaid; Simone Fabbrizzi; Miriam Fahimi; Antonio Ferrara 003; Siamak Ghodsi; Carlos Mougan; Ioanna Papageorgiou; Paula Reyero Lobo; Mayra Russo; Kristen M. Scott; et al. (Ethics Inf. Technol., 2024)