Update
This commit is contained in:
Annkathrin
parent
cd50ac6b56
commit
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\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}
|
||||
|
|
|
|||
|
|
@ -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}
|
||||
Loading…
Reference in New Issue