FixOut… what’s next?
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FixOut is now part of a startup project. Check out the page of the startup project here.
Paper accepted at DSAA’21
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“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.
Course at IDAI 2021 Summer School
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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)
Special sessions at EURO XXI
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Fair and explainable models I (M. Couceiro and L. Galarraga) Fair and explainable models II (M. Couceiro and L. Galarraga)
New demo available
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A demonstration of FixOut on selected datasets is available (thanks to F. Bernier and P. Ringot). Please visit this link.
Invited talk at PDIA’21
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G. Alves gave a talk at PDIA’21 (Perspectives et Défis de l’IA) https://afia.asso.fr/pdia21/
Invited talk at MPML
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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
How to use FixOut: the first tutorial
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The first tutorial of FixOut is now available. Tutorial 1 shows how to use FixOut on tabular data with LIME explanations. Start guide
Plenary talk at AIST 2020
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Fairness Visualizations
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