‘update‘
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Annkathrin
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3 changed files with 38 additions and 42 deletions
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IBC.log
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IBC.log
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This is LuaHBTeX, Version 1.13.0 (TeX Live 2021) (format=lualatex 2021.8.7) 24 SEP 2021 16:52
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This is LuaHBTeX, Version 1.13.0 (TeX Live 2021) (format=lualatex 2021.8.7) 29 SEP 2021 20:26
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restricted system commands enabled.
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**IBC.tex
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(./IBC.tex
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<./iborderctrl-avatar-300x268.jpg>]
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<Polygraph.jpg, id=128, 342.5598pt x 192.59955pt>
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File: Polygraph.jpg Graphic file (type jpg)
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)
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Here is how much of LuaTeX's memory you used:
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100000,794899 words of node,token memory allocated
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2992 words of node memory still in use:
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29 hlist, 7 vlist, 2 rule, 4 local_par, 73 glue, 8 kern, 20 penalty, 184 glyp
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h, 62 attribute, 61 glue_spec, 62 attribute_list, 4 write, 24 pdf_colorstack nod
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Output written on IBC.pdf (35 pages, 8975581 bytes).
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otf>
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Output written on IBC.pdf (35 pages, 8980409 bytes).
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PDF statistics: 370 PDF objects out of 1000 (max. 8388607)
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IBC.pdf
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IBC.tex
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@ -39,7 +39,7 @@
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\begin{frame}
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\begin{figure}
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\includegraphics[scale = 0.8]{iborderctrl-avatar-300x268.jpg}
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\caption*{Quelle: iborderctrl.eu}
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\caption*{\tiny Quelle: iborderctrl.eu}
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\end{figure}
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"The avatar is presented in a uniform to convey an air of authority." (K.Crockett et.al.)
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\end{frame}
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\begin{frame}{Die Geschichte des Lügendetektors - Der Polygraph}
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\begin{figure}
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\includegraphics[scale = 0.8]{Polygraph}
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\caption*{Foto: Sebastian Kahnert/ dpa}
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\caption*{\tiny Foto: Sebastian Kahnert/ dpa}
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\end{figure}
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\end{frame}
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\begin{frame}{Die Geschichte des Lügendetektors - Der Polygraph}
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\begin{frame}{KI, Algorithmen und maschinelles Lernen}
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\begin{figure}
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\begin{center}
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\includegraphics[scale = 0.18]{Ki-Abbildung}
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\includegraphics[scale = 0.16]{Ki-Abbildung}
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\end{center}
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\end{figure}
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\end{frame}
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\begin{frame}{Overfitting}
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\begin{figure}
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\includegraphics[scale = 0.4]{overfitting.png}
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\caption*{Quelle: Sagar Sharma / Towards Data Science}
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\caption*{\tiny Quelle: Sagar Sharma / Towards Data Science}
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\end{figure}
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\end{frame}
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\begin{frame}{Panda oder Gibbon?}
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\begin{figure}
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\includegraphics[scale = 0.55]{panda_gibbon}
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\caption*{Quelle: J. Goodfellow et. al. }
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\includegraphics[scale = 0.54]{panda_gibbon}
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\caption*{\tiny Quelle: J. Goodfellow et. al. }
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\end{figure}
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\end{frame}
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\begin{frame}
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\begin{figure}
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\includegraphics[scale = 0.07]{gibbon}
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\caption*{Quelle: }
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\caption*{\tiny Quelle: }
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\end{figure}
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\end{frame}
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@ -191,8 +191,8 @@ Konsequenz: Diskriminierende Algorithmen (auch Gender Bias, Racial Bias, Neurodi
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Überlagerung von Trends in verschiedenen Untergruppe führt zu falschen Aussagen
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\end{itemize}
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\begin{figure}
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\includegraphics[scale = 0.1]{AggregationBiasBeispiel}
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\caption*{Quelle: Statology.org What is Aggregation Bias?}
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\includegraphics[scale = 0.19]{AggregationBiasBeispiel}
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\caption*{\tiny Quelle: Statology.org What is Aggregation Bias?}
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\end{figure}
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\end{frame}
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@ -205,8 +205,8 @@ Konsequenz: Diskriminierende Algorithmen (auch Gender Bias, Racial Bias, Neurodi
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\begin{frame}{Bias durch Rückkopplung}
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Verstärkung der Diskriminierung durch Rückkopplungsschleife bei Predictive Policing
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\begin{figure}
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\includegraphics[scale = 0.1]{Predpol}
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\caption*{Quelle: Algorithmic Bias and Fairness: Crash Course AI by CrashCourse}
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\includegraphics[scale = 0.23]{Predpol}
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\caption*{\tiny Quelle: Algorithmic Bias and Fairness: Crash Course AI by CrashCourse}
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\end{figure}
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\end{frame}
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