Update README.md

README.md

-# ExpOut
-This project is an extension of LimeOut[1]. It aims at tackle process fairness for classification, while keeping the accuracy level (or improving). 
-More precisely, ExpOut incorporates different explainers.
+# FixOut
+
+FixOut addresses fairness issues of ML models based on decision outcomes, and shows how the simple idea of “feature dropout” followed by an “ensemble approach” can improve model fairness.
+
+Originally, it was conceived to tackle process fairness of ML Models based on decision outcomes (see LimeOut [1]). For that it uses an explanation method to assess a model’s reliance on salient or sensitive features, that is integrated in a human-centered workflow: given a classifier M, a dataset D, a set F of sensitive features and an explanation method of choice, FIXOut outputs a competitive classifier M’ that improves in process fairness as well as in other fairness metrics.
 
 Classifiers available: 
-* Multilayer Perceptron
 * Logistic Regression
 * Random Forest
 * Bagging
 * AdaBoost
+* Multilayer Perceptron
 * Gaussian Mixture
 * Gradiente Boosting
 
 
 Explainers
 * LIME
-* Anchors
+* SHAP
 
 # Example
 `python runner.py --data german.data --trainsize 0.8 --algo mlp --max_features 10 --cat_features 0 2 3 5 6 8 9 11 13 14 16 18 19 --drop 8 18 19 --exp anchors`
 
 # References 
-[1] Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli. LimeOut: An Ensemble Approach To Improve Process Fairness. 2020. ⟨hal-02864059v2⟩
+[1] Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli. LimeOut: An Ensemble Approach To Improve Process Fairness. XKDD Workshop 2020. ⟨hal-02864059v2⟩
+[2] Guilherme Alves, Vaishnavi Bhargava, Miguel Couceiro, Amedeo Napoli. Making ML models fairer through explanations: the case of LimeOut. AIST 2020. ⟨hal-02864059v5⟩
 
 
 ## Dependencies