Algorithmen lernen, indem sie große Datenmengen analysieren und darin wiederkehrende Muster identifizieren. Doch eine große Datenmenge ist nicht automatisch eine gute Datenbasis, betont die Professorin Eirini Ntoutsi. Da sich diese Daten nicht vollständig überprüfen ließen, bestehe die Gefahr, dass bestimmte Gruppen oder Perspektiven unterrepräsentiert würden. Diese Ungleichgewichte führten dann zu systematischen Verzerrungen in den Ergebnissen des Algorithmus, dem sogenannten „Bias“.
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