‘update‘
This commit is contained in:
Annkathrin
parent
81a06c74a5
commit
5a2d103c31
7 changed files with 129 additions and 174 deletions
73
IBC.aux
73
IBC.aux
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@ -61,54 +61,49 @@
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61
IBC.log
61
IBC.log
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@ -1,4 +1,4 @@
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This is LuaHBTeX, Version 1.13.0 (TeX Live 2021) (format=lualatex 2021.8.7) 19 AUG 2021 00:49
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This is LuaHBTeX, Version 1.13.0 (TeX Live 2021) (format=lualatex 2021.8.7) 25 AUG 2021 20:55
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restricted system commands enabled.
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**IBC.tex
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(./IBC.tex
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@ -1001,7 +1001,7 @@ toc) [2
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] [4
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]
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<iborderctrl-avatar-300x268.jpg, id=126, 225.84375pt x 201.75375pt>
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@ -1016,19 +1016,19 @@ Package luatex.def Info: iborderctrl-avatar-300x268.jpg used on input line 41.
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] [8
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]
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File: overfitting.png Graphic file (type png)
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[9
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@ -1041,22 +1041,16 @@ LaTeX Font Info: Font shape `TU/lmtt/bx/n' in size <10> not available
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[]
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[17
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[17
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<./Ergebnis_ibc.png>] [18
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] [20
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@ -1068,20 +1062,21 @@ Package luatex.def Info: Ergebnis_ibc.png used on input line 192.
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(./IBC.aux)
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(./IBC.aux)
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Package rerunfilecheck Info: File `IBC.out' has not changed.
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@ -1089,10 +1084,10 @@ odes
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Output written on IBC.pdf (20 pages, 125391 bytes).
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Output written on IBC.pdf (19 pages, 123784 bytes).
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58
IBC.nav
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IBC.nav
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@ -36,45 +36,41 @@
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13
IBC.out
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IBC.out
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IBC.pdf
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IBC.pdf
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85
IBC.tex
85
IBC.tex
|
|
@ -62,7 +62,7 @@ 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}
|
||||
|
|
@ -77,79 +77,50 @@ iBorderCtrl
|
|||
|
||||
|
||||
\section{Grenzen von KI / Grenzen und Risiken von KI (20min)}
|
||||
\subsection{Beispiele diskriminierender KI}
|
||||
\subsection{KI und Bias}
|
||||
|
||||
\begin{frame}{Beispiele diskriminierender KI}
|
||||
\begin{itemize}
|
||||
\item COMPAS - Risikoabsch\"{a}tzung Gefangener in Florida
|
||||
\begin{itemize}
|
||||
\item Schwarze Gefangene wurden doppelt so häufig falsch als gewaltt\"{a}tig klassifiziert
|
||||
\end{itemize}
|
||||
\item Vermittlung von Stellenanzeigen im MINT-Bereich
|
||||
\begin{itemize}
|
||||
\item Stellenanzeigen wurden häufiger M\"{a}nnern (cis?) angezeigt.
|
||||
\end{itemize}
|
||||
\item Einstellungsverfahren (konkreter Fall amazon?)
|
||||
\item Gesichtserkennung, Stimmerkennung, etc. (konkrete Fälle)
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}{Arten von Bias}
|
||||
Bias = Verzerrung
|
||||
\vspace{0,5cm}
|
||||
\begin{itemize}
|
||||
\item Bias in den Daten
|
||||
\item Bias durch Design des Algorithmus
|
||||
\item Bias durch R\"{u}ckkopplung im Gebrauch
|
||||
\item Bias durch Rückkopplung im Gebrauch
|
||||
\end{itemize}
|
||||
\vspace{0,5cm}
|
||||
Konsequenz: Diskriminierende Algorithmen (auch Gender Bias, Racial Bias, Neurodiversity Bias etc. genannt)
|
||||
\end{frame}
|
||||
|
||||
|
||||
\begin{frame}{Bias in den Daten}
|
||||
\begin{itemize}
|
||||
\item Wie werden bestimmte Eigenschaften gemessen/bestimmt? (Measurement Bias)
|
||||
\item Measurement Bias: Wie werden bestimmte Eigenschaften gemessen/bestimmt?
|
||||
\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 Omitted Variable Bias: Wichtige Daten werden nicht im Modell berücksichtigt
|
||||
\begin{itemize}
|
||||
\item Beispiel
|
||||
\item Beispiel
|
||||
\end{itemize}
|
||||
\item Fehlende Diversit\"{a}t in den Verf\"{u}gbaren Daten (Represenation Bias)
|
||||
\item Represenation Bias: Fehlende Diversität in den verfügbaren Daten
|
||||
\begin{itemize}
|
||||
\item Beispiel
|
||||
\item Beispiel
|
||||
\end{itemize}
|
||||
\item Spezifische Eigenschaften von Minderheiten gehen im gesamten Datensatz unter/Ableiten von Aussagen \"{u}ber Individuen aus Minderheit aus allgemeinem Datensatz (Aggregation Bias)
|
||||
\item Aggregation Bias: Spezifische Eigenschaften von Minderheiten gehen im gesamten Datensatz unter/Ableiten von Aussagen über Individuen aus Minderheit aus allgemeinem Datensatz
|
||||
\begin{itemize}
|
||||
\item Beispiel
|
||||
\item Beispiel
|
||||
\end{itemize}
|
||||
\item Ungleiche Datenmenge verschiedener Untergruppen (Sampling Bias)
|
||||
\begin{itemize}
|
||||
\item Beispiel
|
||||
\end{itemize}
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}{Bias in den Daten}
|
||||
\begin{itemize}
|
||||
\item Historical Bias (Erklärung, Beispiel)
|
||||
\item Population Bias (Erklärung, Beispiel)
|
||||
\item etc.
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}{Bias durch Design}
|
||||
\begin{itemize}
|
||||
\item Algorithmischer Bias (Erklärung, Beispiel)
|
||||
\item Evaluations Bias (Erklärung, Beispiel)
|
||||
\item etc.
|
||||
\item Evaluation Bias: Ergebnis wird an unrealistischen oder unfairen Kriterien gemessen
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}{Bias durch R\"{u}ckkopplung}
|
||||
Beispiel (Profiling?)
|
||||
\begin{frame}{Bias durch Rückkopplung}
|
||||
\begin{itemize}
|
||||
\item Beispiel Profiling
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\subsection{KI und Interpretierbarkeit}
|
||||
|
|
@ -159,8 +130,8 @@ Beispiel (Profiling?)
|
|||
\item Was heißt interpretierbar?
|
||||
\item Warum will mensch das?
|
||||
\item Blackbox-Argument
|
||||
\item Betriebsgeheminis-Argument
|
||||
\item Beispiel: Hack zum Missklassifizieren
|
||||
\item Betriebsgeheimnis-Argument
|
||||
\item Missklassifikationshack
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
|
@ -172,12 +143,14 @@ Beispiel (Profiling?)
|
|||
\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: 73 Prozent in Testdaten vs 93 Prozent in Trainingsdaten, das impliziert Overfitting ist wahrscheinlich
|
||||
\item Historical Bias: höhere Wahrscheinlichkeit zB des Drogenschmuggels bestimmter Gruppen (trifft erst bei Benutzung zu, weil Testdaten zu klein)
|
||||
\item Algorythmischer Bias: zu wenig Einblick
|
||||
\item Evaluations Bias: Testbedingungen entsprechen nicht den Einsatzbedingungen, zB Licht, Diversität
|
||||
\item Bias durch Rückkopplung:
|
||||
\item Overfitting: 73 Prozent in Testdaten vs 93 Prozent in Trainingsdaten, das impliziert Overfitting ist wahrscheinlich
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
\begin{frame}{Bias in iBorderCtrl}
|
||||
\begin{itemize}
|
||||
\item Evaluation Bias: Testbedingungen entsprechen nicht den Einsatzbedingungen, zB Licht, Diversität
|
||||
\item Bias durch Rückkopplung: Wenn Einsatzdaten wieder eingespeist werden, dann: LD klassifiziert mehr Benachteiligte als lügend, Überprüfungsbeamt*in ebenso und gibt Daten zurück
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
|
@ -195,17 +168,15 @@ Beispiel (Profiling?)
|
|||
\begin{frame}{Politische Einordnung}
|
||||
\begin{itemize}
|
||||
\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?)
|
||||
\item Entwicklung Ethischer Normen für KI (EU KI Standards, Gesellschaft für Informatik)
|
||||
\end{itemize}
|
||||
\end{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
|
||||
\item Gibt es Ziele und Werte die nicht von der Wissenschaft vorgegeben werden, sondern die wir uns selbst setzen müssen?
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
\end{document}
|
||||
|
|
|
|||
13
IBC.toc
13
IBC.toc
|
|
@ -6,10 +6,9 @@
|
|||
\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}
|
||||
\beamer@subsectionintoc {3}{1}{KI und Bias}{10}{0}{3}
|
||||
\beamer@subsectionintoc {3}{2}{KI und Interpretierbarkeit}{14}{0}{3}
|
||||
\beamer@sectionintoc {4}{Bias Bingo mit / Bias in iBorderCtrl (20min)(interaktiv?)}{15}{0}{4}
|
||||
\beamer@sectionintoc {5}{Ausblick und Diskussion(20min)}{18}{0}{5}
|
||||
\beamer@subsectionintoc {5}{1}{Politische Einordnung}{18}{0}{5}
|
||||
\beamer@subsectionintoc {5}{2}{Wie und wofür forschen wir?}{19}{0}{5}
|
||||
|
|
|
|||
Loading…
Reference in a new issue