From 4ad65f91926a75ee0e761c5c60b7ca74e43910d3 Mon Sep 17 00:00:00 2001
From: Jan Aalmoes <jan.aalmoes@inria.fr>
Date: Sat, 7 Dec 2024 18:20:32 +0100
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+{
+    \usebackgroundtemplate{\includegraphics[width=\paperwidth]{images/background/card/background.pdf}}
+\begin{frame}
+    %\vspace{70px}
+    \hspace{70px}
+    \begin{minipage}{250px}
+        \Large
+        \textcolor{accent}{
+            Evaluation expérimentale de l'utilisation de l'équitée comme mécanisme de protéction de l'attribut sensible.
+        }
+    \end{minipage}
+\end{frame}
+}
+\begin{frame}
+    \begin{figure}
+        \centering
+        \input{tikz/data}
+        \label{fig:aia-data}
+    \end{figure}
+\end{frame}
+
+\begin{frame}
+    \frametitle{Experimental validation on prediction: results}
+    \begin{figure}
+        \captionsetup{singlelinecheck=off}
+        \centering
+        \begin{subfigure}{0.4\textwidth}
+            \centering
+            \scriptsize
+            \begin{itemize}
+                \item \emph{Labeled Faces in the Wild (images)}
+                \item ML = Convolutional Neural Network 
+            \end{itemize}
+            \includegraphics[width=150px]{images/figures/advdebias/lfw/lfw_advdeb_attack_hard_sex.pdf}
+        \end{subfigure}
+        \hspace{0.1\textwidth}
+        \begin{subfigure}{0.4\textwidth}
+            \centering
+            \scriptsize
+            \begin{itemize}
+                \item \emph{COMPAS recidivism dataset (tabular)}
+                \item ML = Random Forest
+            \end{itemize}
+            \includegraphics[width=150px]{images/figures/advdebias/compas/compas_advdeb_attack_hard_sex.pdf}
+        \end{subfigure}
+    \end{figure}
+    \vspace{10px}
+
+    \scriptsize
+    \begin{tabular}{lll}
+        &\emph{Regularization}&\emph{Value}\\ 
+        \emph{Baseline}&None&Attack result\\
+        \emph{Theoretical}&Adversarial debiasing&$\frac{1}{2}(1+DemParLvl)$\\
+        \emph{Empirical}&Adversarial debiasing&Attack result\\
+    \end{tabular}
+\normalsize
+    \hspace{10px}
+Attack surface = $1_{[\tau,1]}\circ f\circ X$.
+\end{frame}
+
+\begin{frame}
+    \frametitle{Experimental validation on logit: building an attack}
+    \begin{enumerate}
+        \item On part.
+        \item Build a random forest on this dataset.
+        \item Ajust the threshold to take into account class imbalance.
+    \end{enumerate}
+\end{frame}
+
+\begin{frame}
+    \frametitle{Experimental validation on logit: results}
+    \begin{figure}
+        \captionsetup{singlelinecheck=off}
+        \centering
+        \begin{subfigure}{0.4\textwidth}
+            \centering
+            \scriptsize
+            \begin{itemize}
+                \item \emph{Labeled Faces in the Wild (images)}
+                \item ML = Convolutional Neural Network 
+            \end{itemize}
+            \includegraphics[width=150px]{images/figures/advdebias/lfw/lfw_advdeb_attack_soft_experimental_sex.pdf}
+        \end{subfigure}
+        \hspace{0.1\textwidth}
+        \begin{subfigure}{0.4\textwidth}
+            \centering
+            \scriptsize
+            \begin{itemize}
+                \item \emph{COMPAS recidivism dataset (tabular)}
+                \item ML = Random Forest
+            \end{itemize}
+            \includegraphics[width=150px]{images/figures/advdebias/compas/compas_advdeb_attack_soft_experimental_sex.pdf}
+        \end{subfigure}
+    \end{figure}
+    \vspace{10px}
+
+    \scriptsize
+    \begin{tabular}{lll}
+        &\emph{Regularization}&\emph{Value}\\ 
+        \emph{Baseline}&None&Attack result\\
+        \emph{AdvDebias}&Adversarial debiasing&Attack result\\
+    \end{tabular}
+\normalsize
+    \hspace{10px}
+Attack surface = $f\circ X$.
+\end{frame}
-- 
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