Investigating Bias with a Synthetic Data Generator: Empirical Evidence and Philosophical Interpretation. - Alessandro Castelnovo; Riccardo Crupi; Nicole Inverardi; Daniele Regoli; Andrea Cosentini (BEWARE@AI*IA, 2022)

An Open-Source Toolkit to Generate Biased Datasets. - Joachim Baumann 002; Alessandro Castelnovo; Riccardo Crupi; Nicole Inverardi; Daniele Regoli (EWAF, 2023)

Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias. - Joachim Baumann 002; Alessandro Castelnovo; Riccardo Crupi; Nicole Inverardi; Daniele Regoli (FAccT, 2023)

Bias On Demand: Investigating Bias with a Synthetic Data Generator. - Joachim Baumann 002; Alessandro Castelnovo; Andrea Cosentini; Riccardo Crupi; Nicole Inverardi; Daniele Regoli (IJCAI, 2023)

Leveraging Group Contrastive Explanations for Handling Fairness. - Alessandro Castelnovo; Nicole Inverardi; Lorenzo Malandri; Fabio Mercorio; Mario Mezzanzanica; Andrea Seveso (xAI (3), 2023)

Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms. - Alessandro Castelnovo; Nicole Inverardi; Gabriele Nanino; Ilaria Giuseppina Penco; Daniele Regoli (CoRR, 2023)

Fair Enough? A Map of the Current Limitations of the Requirements to Have Fair Algorithms (Short paper). - Daniele Regoli; Alessandro Castelnovo; Nicole Inverardi; Gabriele Nanino; Ilaria Giuseppina Penco (DP@AI*IA, 2024)

Fundamental Rights and AI Impact Assessment: a proposal for a new quantitative approach. - Nicole Inverardi; Samuele Bertaina; Ilaria Biganzoli; Andrea Cosentini; Rachele Desiante; Dario Fontanella; Ilaria Giuseppina Penco (IJCNN, 2024)

Fundamental rights and artificial intelligence impact assessment: A new quantitative methodology in the upcoming era of AI Act. - Samuele Bertaina; Ilaria Biganzoli; Rachele Desiante; Dario Fontanella; Nicole Inverardi; Ilaria Giuseppina Penco; Andrea Claudio Cosentini (Comput. Law Secur. Rev., 2025)