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Annkathrin 2021-08-12 12:01:15 +02:00
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@ -4,9 +4,8 @@
\usepackage{graphicx}
\usepackage{caption}
\title{\texttt{iBorderCtrl} /exit iBorderCtrl}
\subtitle{KI im Kontext von Sicherheitspolitik/ \\
Wie die EU versucht rassistische Migrationspolitik durch einen pseudowissenschaftlichen KI-Lügendetektor zu legitimieren}
\title{\texttt{Smart Borders?}}
\subtitle{Wie die EU versucht Grenzübergänge mit einem diskriminierenden KI-Lügendetektor zu regulieren.}
\author{verschiedene\footnote{\texttt{AG Link}}}
\begin{document}
@ -23,10 +22,10 @@ Wie die EU versucht rassistische Migrationspolitik durch einen pseudowissenschaf
\subsection{Akteure und Organisationsstruktur}
\begin{frame}{Akteure und Organisationsstruktur}
\begin{itemize}
\item Horizon 2020, (auch Roborder)
\item Horizon 2020 (auch Roborder)
\item Tresspass etc.
\item Finanzierung
\item beteiligte Unternehmen? Beteiligte Universitäten
\item Beteiligte Forschungseinrichtungen, beteiligte Unternehmen?
\item Aktueller Entwickllungsstand
\end{itemize}
\end{frame}
@ -44,18 +43,16 @@ Wie die EU versucht rassistische Migrationspolitik durch einen pseudowissenschaf
\begin{frame}{Geschichte des Lügendetektor}
\begin{itemize}
\item Polygraph (Genauigkeit: beinahe zufällig (Saxe , Ben-Shakhar, 1999)
\item Mirco Expression etc.
\item Polygraph (Genauigkeit: beinahe zufällig (Saxe, Ben-Shakhar, 1999)
\item Mirco Expressions etc.
\end{itemize}
Infos in : The politics of deceptive borders: biomarkers of deceit and the case of
Infos in: The politics of deceptive borders: biomarkers of deceit and the case of
iBorderCtrl
\end{frame}
\section{Grundlagen KI (15min)}
\subsection{Begriffsdefinitionen}
\begin{frame}{Begriffsdefinitionen}
\subsection{Algorithmen, KI und Neuronale Netze}
\begin{frame}{Algorithmen, KI und Neuronale Netze}
\begin{itemize}
\item Algorithmus
\item KI / Machine Learning
@ -65,12 +62,12 @@ iBorderCtrl
\begin{frame}{Aufbau der Datensätze}
\begin{itemize}
\item supervised learning -> Datenpaare
\item Supervised learning -> 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}

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\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}