{
    \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}
