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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} diff --git a/IBC.toc b/IBC.toc index 6f7e348..84f8e91 100644 --- a/IBC.toc +++ b/IBC.toc @@ -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}