‘update‘

This commit is contained in:
Annkathrin 2021-10-08 11:52:04 +02:00
parent 8f5be49574
commit 199b9e7662
8 changed files with 454 additions and 324 deletions

201
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19
IBC.tex
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@ -60,7 +60,7 @@
\subsection{iBorderCtrl}
\begin{frame}{iBorderCtrl}
\begin{itemize}
\item iBorderCtrl = Intelligent Portable Controll System
\item iBorderCtrl = Intelligent Portable Control System
\item Dauer: 2016-2019
\item Budget: 4 Mio. €
\item 13 EU weite Unternehmen, Forschungs- und Bildungseinrichtungen
@ -123,7 +123,7 @@
\begin{frame}{Die Geschichte des Lügendetektors - Die Rolle von KI}
\begin{itemize}
\item Versuche Mikroexpressionen und ähnliches mit KI zu klassifizieren,
\item Versuche Mikroexpressionen und Ähnliches mit KI zu klassifizieren,
\item Nutzung für die Optimierung von Werbeanzeigen (Facebook),
\item Projekt AVATAR (US-Projekt für Grenzkontrollen)
\end{itemize}
@ -264,17 +264,17 @@ Konzeptionelle Mängel in iBorderControl
\begin{frame}{Bias in iBorderCtrl}
\begin{itemize}
\item<1-> \alert<1>{Measurement Bias:} Stress beim Grenzübergang löst u.U. gleiche Symptome wie Lügen aus
\item<2-> \alert<2>{Omitted Variable Bias:} Lügen/nicht Lügen zeigt sich in nicht erfassten Phänomenen.Es gibt keine zuverlässigen Theorien dazu, welche physiologischen Prozesse Lügen eindeutig identifizierbar machen.
\item<2-> \alert<2>{Omitted Variable Bias:} Lügen/nicht Lügen zeigt sich in nicht erfassten Phänomenen.Es gibt keine zuverlässigen Theorien dazu, welche physiologischen Merkmale Lügen eindeutig identifizierbar machen.
\item<3-> \alert<3>{Representation Bias:} fehlende Diversität in Hautfarbe, Geschlecht, Neurodiversität, Behinderungen, gesundheitlichen Faktoren, Narben etc.
\item<4-> \alert<4>{Aggregation Bias:} Reaktionen (z.B. Mikroexpressionen) nicht konsistent. Gleiches Verhalten bei unterschiedlichen Individuen kann Unterschiedliches bedeuten.
\item<5-> \alert<5>{Overfitting:} Trefferquote von 73 Prozent in Testdaten vs 93 Prozent in Trainingsdaten deutet auf Overfitting hin
\item<4-> \alert<4>{Aggregation Bias:} Reaktionen (z.B. Mikroexpressionen) nicht konsistent. Gleiches Verhalten bei unterschiedlichen Individuen/Gruppen kann Unterschiedliches bedeuten.
\item<5-> \alert<5>{Overfitting:} Trefferquote von 93 Prozent in Trainingsdaten vs 73 Prozent in Testdaten deutet auf Overfitting hin
\end{itemize}
\end{frame}
\begin{frame}{Bias in iBorderCtrl}
\begin{itemize}
\item<1-> \alert<1>{Evaluation Bias:} Testbedingungen entsprechen nicht den Einsatzbedingungen, zB Licht, Diversität
\item<2-> \alert<2>{Bias durch Rückkopplung:} Wenn Einsatzdaten wieder eingespeist werden, dann klassifiziert der Lügendetektor mehr Benachteiligte als lügend, Überprüfungsbeamt*innen bestätigen die Entscheidung und geben die Daten zurück an das System.
\item<1-> \alert<1>{Evaluation Bias:} Testbedingungen entsprechen nicht den Einsatzbedingungen, zB Lichtverhältnisse
\item<2-> \alert<2>{Bias durch Rückkopplung:} Verzerrte Einsatzdaten werden von Überprüfungsbeamt*innen bestätigt und wieder in das System eingespeist, wodurch immer mehr Benachteiligte als lügend klassifiziert werden
\end{itemize}
\end{frame}
@ -317,6 +317,11 @@ Konzeptionelle Mängel in iBorderControl
\begin{frame}{Wie und wofür forschen wir?}
\begin{itemize}
\item Wissenschaft kann instrumentalisiert werden
\begin{itemize}
\item mangelnde Rechenschaft (Argument der Blackbox, des Betriebsgeheimnisses)
\item Transparenzinitiative Johanna Zitt
\item FATML.org, Fairness, Accountability and Transparency in ML
\end{itemize}
\item Wir brauchen mehr kognitive Diversität in Forschungseinrichtungen
\item Was ist dein wissenschaftlicher Standard und worauf gründet er?
\end{itemize}

30
IBC.toc
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@ -1,14 +1,16 @@
\beamer@sectionintoc {1}{Was ist iBorderCtrl?}{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}{11}{0}{2}
\beamer@subsectionintoc {2}{1}{KI, Algorithmen und maschinelles Lernen}{11}{0}{2}
\beamer@subsectionintoc {2}{2}{Overfitting}{12}{0}{2}
\beamer@sectionintoc {3}{Grenzen und Risiken von KI}{13}{0}{3}
\beamer@subsectionintoc {3}{1}{KI und Interpretierbarkeit}{13}{0}{3}
\beamer@subsectionintoc {3}{2}{KI und Bias}{18}{0}{3}
\beamer@sectionintoc {4}{Bias in iBorderCtrl}{25}{0}{4}
\beamer@sectionintoc {5}{Ausblick und Diskussion}{34}{0}{5}
\beamer@subsectionintoc {5}{1}{Politische Einordnung}{34}{0}{5}
\beamer@subsectionintoc {5}{2}{Wie und wofür forschen wir?}{35}{0}{5}
\beamer@sectionintoc {1}{Smart ... was? - Einführung in Smart Borders}{3}{0}{1}
\beamer@subsectionintoc {1}{1}{Smart Borders Package der EU}{3}{0}{1}
\beamer@subsectionintoc {1}{2}{iBorderCtrl}{6}{0}{1}
\beamer@subsectionintoc {1}{3}{Geschichte des Lügendetektor}{9}{0}{1}
\beamer@sectionintoc {2}{Grundlagen KI}{14}{0}{2}
\beamer@subsectionintoc {2}{1}{KI, Algorithmen und maschinelles Lernen}{14}{0}{2}
\beamer@subsectionintoc {2}{2}{Overfitting}{15}{0}{2}
\beamer@sectionintoc {3}{Grenzen und Risiken von KI}{16}{0}{3}
\beamer@subsectionintoc {3}{1}{KI und Interpretierbarkeit}{16}{0}{3}
\beamer@subsectionintoc {3}{2}{KI und Bias}{21}{0}{3}
\beamer@sectionintoc {4}{Bias in iBorderCtrl}{28}{0}{4}
\beamer@sectionintoc {5}{Ausblick und Diskussion}{37}{0}{5}
\beamer@subsectionintoc {5}{1}{Politische Einordnung}{37}{0}{5}
\beamer@subsectionintoc {5}{2}{Wie und wofür forschen wir?}{39}{0}{5}
\beamer@sectionintoc {6}{Quellen}{40}{0}{6}
\beamer@sectionintoc {7}{Anhang}{41}{0}{7}