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Beeinflussung durch Künstliche Intelligenz

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Eine wissenschaftliche Perspektive auf die achtlose Anwendung der Algorithmen des maschinellen Lernens und der künstlichen Intelligenz, z.B. in personalisierten Nachrichtenempfehlungssystemen oder Risikosoftware im US-Justizsystem und die Ursache ihrer Fehlvorraussagen.

Published in: Data & Analytics
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Beeinflussung durch Künstliche Intelligenz

  1. 1. Beeinflussung durch Künstliche Intelligenz Über die Banalität der Beeinflussung und das Leben mit Algorithmen Gemälde von Sarina Walter mit freundlicher Genehmigung der Künstlerin
  2. 2. Die Menschen befürchten, dass Computer zu intelligent werden und die Welt erobern. Aber Computer sind zu dumm und haben die Welt bereits erobert. Kate Crawford
  3. 3. Über uns AMSTERDAM MACHINE LEARNING LAB UNIVERSITEIT VAN AMSTERDAM KAREN ULLRICH HENDRIK HEUER INSTITUT FÜR INFORMATIONSMANAGEMENT BREMEN UNIVERSITÄT BREMEN karenullrich.info @karen_ullrich hen-drik.de @hen_drik
  4. 4. Spam erkennen IMPERATIVE PROGRAMMIERUNG MASCHINELLES LERNEN if "V!agrå" in mail: return "Spam" else: return "Ham" 1. prognostiziere für viele Beispiele, ob Nachricht Spam ist 2. berechne Fehler 3. ändere Parameter, 
 um Fehler zu verringern 4. goto 1
  5. 5. Brustkrebs erkennen Example: breast cancer classification Plot of two features: mean area vs. mean concave points, for two classes J. Zico Kolter, 15-388/688, Practical Data Science
  6. 6. Brustkrebs erkennen Linear classification example Linear classification ≡ “drawing line separating classes” J. Zico Kolter, 15-388/688, Practical Data Science
  7. 7. Persönlichkeitsmerkmale erkennen Weiße/r oder Afroamerikaner/in: 95% Männlich oder Weiblich: 93% Heterosexuell oder homosexuell: 88% Demokrat oder Republikaner: 85% Christlich oder muslimisch: 82% Drogenerfahrung: 65% - 73% Ehe der Eltern hat bis zum 21. Lebensjahr gehalten: 60% Kosinski et al.: Private traits and attributes are predictable from digital records of human behavior
  8. 8. Persönlichkeitsmerkmale erkennen Kosinski et al.: Private traits and attributes are predictable from digital records of human behavior
  9. 9. ProPublica: Machine Bias Verbrechen vorhersagen 3 LOW RISK 8 HIGH RISK
  10. 10. LOW RISK HIGH RISK X y' '
  11. 11. Verbrechen vorhersagen Deutschlandfunk: Algorithmen im US- Justizsystem Schicksalsmaschinen von Tom Schimmeck
  12. 12. Algorithmic News Curation Paul Butler: Visualizing Friendships
  13. 13. Algorithmic News Curation Paul Butler: Visualizing Friendships
  14. 14. Wieviel Vertrauen ist angebracht? Wann scheitern die Algorithmen? Gemälde von Sarina Walter mit freundlicher Genehmigung der Künstlerin
  15. 15. Bias der Daten Bias des Modells
  16. 16. Rechtschaffende 1100 Fotos aus verschiedenen Quellen des Internets. 1800 Fotos, Chinesen, 18-55, keine Gesichtshaare, Narben, Tattoos Kriminelle 700 Fotos verurteilter Krimineller, bereitgestellt durch die Polizei.
  17. 17. Verzerrung der Stichprobe Anzahl der Daten Melde-Bias Ausgewählte Merkmale, die ein Problem beschreiben
  18. 18. Bias der Daten Bias des Modells
  19. 19. “No Free Lunch” - Theorem Betrachtet man alle möglichen Situationen, 
 funktioniert kein Modell besser als alle anderen. Wir müssen Annahmen machen, um Szenarien zu beschreiben und Modelle zu vereinfachen (Modell-Bias). Aber: Annahmen versagen in gewissen Situationen. Unsere Aufgabe: Annahmen benennen und überprüfen, ob sie valide sind.
  20. 20. Zielsetzung: Was ist Erfolg? Ziele basieren auf Annahmen Problem: Richtigkeit einer Übersetzung Wort für Wort? Paragraph für Paragraph? Synonyme?
  21. 21. Ziele basieren auf Annahmen Problem: Richtigkeit einer Übersetzung Satz für Satz Sätze sind unabhängig Nur das wahrscheinlichste Wort/ Phrase wird als richtig bewertet Zielsetzung: Was ist Erfolg?
  22. 22. Zielsetzung als Bias
  23. 23. Bias der Daten Bias des Modells
  24. 24. Aktives Lernen Human in the Loop Me Anreicherung des Datensets Algorithmus lernt durch Maschinelles Lernen Bei unsicheren Daten wird ein Experte befragt Me
  25. 25. Übertragendes Lernen Ziel: Aufgabe erledigen Problem: Wenige Daten Bias Ähnliche Aufgabe, die erfolgreich gelöst werden kann
  26. 26. Extrapolieren ? Interpolieren
  27. 27. Interpolieren
  28. 28. Extrapolieren ??
  29. 29. RARE EVENT URSACHE EFFEKT ?
  30. 30. Gemälde von Sarina Walter mit freundlicher Genehmigung der Künstlerin KAREN ULLRICH karenullrich.info @karen_ullrich HENDRIK HEUER hen-drik.de @hen_drik Beeinflussung durch Künstliche Intelligenz

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