From 5a2d103c318e6b1bb4d07e1c51afefbe2683dd3c Mon Sep 17 00:00:00 2001 From: Annkathrin Date: Wed, 25 Aug 2021 20:59:06 +0200 Subject: [PATCH] =?UTF-8?q?=E2=80=98update=E2=80=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- IBC.aux | 73 +++++++++++++++++++++++------------------------- IBC.log | 61 +++++++++++++++++++--------------------- IBC.nav | 58 ++++++++++++++++++-------------------- IBC.out | 13 ++++----- IBC.pdf | Bin 125391 -> 123784 bytes IBC.tex | 85 +++++++++++++++++++------------------------------------- IBC.toc | 13 ++++----- 7 files changed, 129 insertions(+), 174 deletions(-) diff --git a/IBC.aux b/IBC.aux index 2c2f774..77c6eaa 100644 --- a/IBC.aux +++ b/IBC.aux @@ -61,54 +61,49 @@ \@writefile{nav}{\headcommand {\beamer@sectionpages {7}{9}}} \@writefile{nav}{\headcommand {\beamer@subsectionpages {9}{9}}} \@writefile{nav}{\headcommand {\sectionentry {3}{Grenzen von KI / Grenzen und Risiken von KI (20min)}{10}{Grenzen von 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sqCuA+3a=u9S*p?nMZSA6Pk8h1$%ZED@4%2Cv$Jz>BT-R_f001?FY>VNYXATM diff --git a/IBC.tex b/IBC.tex index dfee1a5..a8be13a 100644 --- a/IBC.tex +++ b/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} diff --git a/IBC.toc b/IBC.toc index 59ed3a9..4412a94 100644 --- a/IBC.toc +++ b/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}