diff --git a/README.qmd b/README.qmd
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+++ b/README.qmd
@@ -10,7 +10,7 @@ This repository contains all code, notebooks, data and empirical results for our
 
 The chart below illustrates what we define as macrodynamics in Algorithmic Recourse: (a) we have a simple linear classifier trained for binary classification where samples from the negative class ($y=0$) are marked in blue and samples of the positive class ($y=1$) are marked in orange; (b) the implementation of AR for a random subset of individuals leads to a noticable domain shift; (c) as the classifier is retrained we observe a corresponding model shift; (d) as this process is repeated, the decision boundary moves away from the target class.
 
-![](cover.png)
+![](paper/www/poc.png)
 
 ## Abstract
 
diff --git a/_quarto.yml b/_quarto.yml
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@@ -43,7 +43,7 @@ format:
   html:
     theme: cosmo
   epub:
-    cover-image: cover.png
+    cover-image: paper/www/poc.png
 
 execute: 
   freeze: auto
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Binary files a/cover.png and /dev/null differ
diff --git a/intro.qmd b/intro.qmd
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@@ -145,6 +145,14 @@ display(plt)
 
 ## Mitigation Strategies
 
+In the paper, we propose three simple mitigation strategies:
+
+1. More Conservative Decision Thresholds
+2. Classifier Preserving ROAR
+3. Gravitational Counterfactual Explanations 
+
+@fig-mitigate shows an illustrative example that demonstrates the differences in counterfactual outcomes when using the various mitigation strategies compared to the baseline approach, that is, Wachter with $\gamma=0.5$: choosing a higher decision threshold pushes the counterfactual a little further into the target domain; this effect is even stronger for ClaPROAR; finally, using the Gravitational generator the counterfactual ends up all the way inside the target domain. 
+
 ```{julia}
 #| output: true
 #| label: fig-mitigate