FixOut is now part of a startup project. Check out the page of the startup project here.
“Reducing Unintended Bias of ML Models on Tabular and Textual Data” has been accepted at DSAA 2021. Pre-print and full versions will be available soon.
M. Couceiro and C. Palamidessi will give the course Addressing algorithmic fairness through metrics and explanations in the First Inria-DFKI European Summer School on AI (IDAI 2021). Material Introduction Part 1 – Notions of fairness (C. Palamidessi) Part 2 – Addressing unfairness through unawareness (M. Couceiro)
Fair and explainable models I (M. Couceiro and L. Galarraga) Fair and explainable models II (M. Couceiro and L. Galarraga)
A demonstration of FixOut on selected datasets is available (thanks to F. Bernier and P. Ringot). Please visit this link.
G. Alves gave a talk at PDIA’21 (Perspectives et Défis de l’IA) https://afia.asso.fr/pdia21/
M. Couceiro will give a talk at the IST seminar series on Mathematics, Physics & Machine Learning (MPML). https://mpml.tecnico.ulisboa.pt/seminars?id=5976
The first tutorial of FixOut is now available. Tutorial 1 shows how to use FixOut on tabular data with LIME explanations. Start guide