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Datenpaare \item Training, Validation and Test Data \end{itemize} \end{frame} -\subsection{overfitting} +\subsection{Overfitting} \begin{frame}{Overfitting} \begin{figure} \includegraphics[scale = 0.4]{overfitting.png} @@ -80,7 +77,7 @@ iBorderCtrl \section{Grenzen von KI / Grenzen und Risiken von KI (20min)} -\subsection{Beispiele Diskriminierender KI} +\subsection{Beispiele diskriminierender KI} \subsection{KI und Bias} \begin{frame}{Beispiele diskriminierender KI} @@ -116,11 +113,11 @@ Konsequenz: Diskriminierende Algorithmen (auch Gender Bias, Racial Bias, Neurodi \begin{itemize} \item COMPAS: Verhaftungen auch von Familie etc. wurden genutzt um Risiko zu bewerten \end{itemize} -\item wichtige Daten werden nicht im Modell berücksichtigt (Omitted Variable Bias) +\item Wichtige Daten werden nicht im Modell berücksichtigt (Omitted Variable Bias) \begin{itemize} \item Beispiel \end{itemize} -\item fehlende Diversit\"{a}t in den Verf\"{u}gbaren Daten (Represenation Bias) +\item Fehlende Diversit\"{a}t in den Verf\"{u}gbaren Daten (Represenation Bias) \begin{itemize} \item Beispiel \end{itemize} @@ -152,45 +149,61 @@ Konsequenz: Diskriminierende Algorithmen (auch Gender Bias, Racial Bias, Neurodi \end{frame} \begin{frame}{Bias durch R\"{u}ckkopplung} -Beispiel (Profiling? ) +Beispiel (Profiling?) \end{frame} \subsection{KI und Interpretierbarkeit} \begin{frame}{Interpretierbarkeit} \begin{itemize} -\item Was heißt interpretierbar -\item Warum will mensch das +\item Was heißt interpretierbar? +\item Warum will mensch das? \item Blackbox-Argument -\item Betriebsgeheminis +\item Betriebsgeheminis-Argument \item Beispiel: Hack zum Missklassifizieren \end{itemize} \end{frame} -\section{iBorderCtrl / Bias Bingo mit iBorderCtrl (20min)(interaktiv?)} +\section{Bias Bingo mit / Bias in iBorderCtrl (20min)(interaktiv?)} \begin{frame}{Bias in iBorderCtrl} +\begin{itemize} +\item Measurement Bias: Wie wird entschieden ob jemand gelogen hat o.Ä.? +\item Omitted Variable Bias: Nervosität durch Stress beim Grenzübergang +\item Representation Bias: fehlende Diversität race, gender, neurdiversity, disability, health, scars +\item Aggregation Bias: s. representation bias, teilweiser Versuch der Gegensteuerung +\item Sampling Bias: ungleiche Datenmengen +\item Overfitting +\item ... +\end{itemize} \end{frame} -\begin{frame}{Testerbenisse} +\begin{frame}{Testergebnisse} \begin{itemize} -\item Anzahl verschiedener Personen in der Testdatenmenge : 1 +\item Anzahl verschiedener Personen in der Testdatenmenge: 1 \item Emprische Varianz der Tests: \end{itemize} \begin{figure} \includegraphics[scale = 0.36]{Ergebnis_ibc} \end{figure} \end{frame} -\section{Ausblick/ Diskussion(20min)} -\subsection{politische Einordnung} -\begin{frame}{politische Einordnung} +\section{Ausblick und Diskussion(20min)} +\subsection{Politische Einordnung} +\begin{frame}{Politische Einordnung} \begin{itemize} -\item unfreiwillige Datenerhebung zur "Verbesserung" des Algorithmus -\item Entwicklung Ethischer Normen für KI +\item Unfreiwillige Datenerhebung zur "Verbesserung" des Algorithmus +\item Entwicklung Ethischer Normen für KI (EU KI Standards, Gesellschaft für Informatik, The Ethical Algorithm von Kearns, Roth?) \end{itemize} \end{frame} -\subsection{Wie und wofür forschen wir? /Wissenschaft und Ethik} -\begin{frame} +\subsection{Wie und wofür forschen wir?} +\begin{frame}{Wie und wofür forschen wir?} +\begin{itemize} +\item Instrumentalisierung von Wissenschaft +\item https://youtu.be/f9WkKKZXvgA?t=3411 +\item Was ist mein wissenschaftlicher Standard und worauf gründet er? +\item Gibt es Ziele und Werte die nicht von der Wissenschaft vorgegen werden, sondern die wir uns selbst setzen müssen? +\item Hinweis auf Transparenzinitiative der Senatorin Johanna +\end{itemize} \end{frame} \end{document} diff --git a/IBC.toc b/IBC.toc new file mode 100644 index 0000000..59ed3a9 --- /dev/null +++ b/IBC.toc @@ -0,0 +1,15 @@ +\beamer@sectionintoc {1}{Was ist iBorderCtrl? (15min)}{3}{0}{1} +\beamer@subsectionintoc {1}{1}{Akteure und Organisationsstruktur}{3}{0}{1} +\beamer@subsectionintoc {1}{2}{Silent Talker}{4}{0}{1} +\beamer@subsectionintoc {1}{3}{Geschichte des Lügendetektor}{6}{0}{1} +\beamer@sectionintoc {2}{Grundlagen KI (15min)}{7}{0}{2} +\beamer@subsectionintoc {2}{1}{Algorithmen, KI und Neuronale Netze}{7}{0}{2} +\beamer@subsectionintoc {2}{2}{Overfitting}{9}{0}{2} +\beamer@sectionintoc {3}{Grenzen von KI / Grenzen und Risiken von KI (20min)}{10}{0}{3} +\beamer@subsectionintoc {3}{1}{Beispiele diskriminierender KI}{10}{0}{3} +\beamer@subsectionintoc {3}{2}{KI und Bias}{10}{0}{3} +\beamer@subsectionintoc {3}{3}{KI und Interpretierbarkeit}{16}{0}{3} +\beamer@sectionintoc {4}{Bias Bingo mit / Bias in iBorderCtrl (20min)(interaktiv?)}{17}{0}{4} +\beamer@sectionintoc {5}{Ausblick und Diskussion(20min)}{19}{0}{5} +\beamer@subsectionintoc {5}{1}{Politische Einordnung}{19}{0}{5} +\beamer@subsectionintoc {5}{2}{Wie und wofür forschen wir?}{20}{0}{5}