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pres.tex
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{Problem definition}
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\begin{itemize}
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\item People often use Machine Learning models for predictions
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\item Blindly trusting a prediction can lead to poor decision making
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\item We seek to understand the reasons behind predictions
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\begin{itemize}
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\item As well as the model doing the predictions
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\end{itemize}
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\end{itemize}
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\center
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\includegraphics[scale=0.2]{graphics/doctor_pred.png}
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\end{frame}
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%\subsection{Previous Solutions}
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\begin{frame}
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\frametitle{Previous Solutions}
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\end{frame}
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\begin{frame}
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\frametitle{Intepretability}
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\frametitle{Properties of a good explanation}
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\begin{itemize}
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\item It should be \emph{intepretable}:
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\begin{itemize}
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\item They must provide qualitative understanding between the input variables and the response
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\item They must take into account the users limitations
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\item Use a representation understandable to humans
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\item Could be a binary vector indicating presence or absence of a word
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\item Could be a binary vector indicating presence of absence of super-pixels in an image
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\end{itemize}
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\item It should have \emph{fidelity}:
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\begin{itemize}
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\item Essentially means the model should be faithful.
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\item Local fidelity does not imply global fidelity
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\item The explanation should aim to correspond to how the model behaves in the vicinity of the instance being predicted
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\end{itemize}
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\item It should be \emph{model-agnostic}:
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\begin{itemize}
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\item The explanation should be blind to what model is underneath
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{Fidelity}
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\end{frame}
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\subsection{Explaining Predictions}
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\begin{frame}
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\frametitle{The Fidelity-Interpretability Trade-off}
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\end{frame}
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% \subsubsection{Examples}
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\begin{frame}
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% \frametitle{Sparse Linear Explanations}
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