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style=\"padding-top: 8.0px;\"\u003EGrundlagen\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-56bcd77749","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Datenethik: Prinzipien und Praktiken für eine verantwortungsvolle Datennutzung"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-b057761bbe","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EDatenethik hilft Organisationen nicht nur bei der Entscheidung, ob die Datennutzung legal ist, sondern auch, ob sie über die Erfassung, Speicherung, Analyse, Freigabe und KI-Entwicklung hinweg angemessen, verhältnismäßig und verantwortbar ist. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","gridClassNames":"aem-Grid 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zur Datenethik:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-5f19784226","additionalClasses":"related-topics ","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/policy/\"\u003EData-Governance-Richtlinie\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/templates/\"\u003ERichtlinienvorlagen (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/policy/standards/\"\u003EGovernance-Standards\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/audit/\"\u003EData-Governance-Audit (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/data-governance/data-privacy/\"\u003EDatenschutz\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_549182334":{"id":"flexible-column-container-011e901d3c","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","id":"hub-body-content","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text":{"id":"text-d256b9552a","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EDATENETHIK DEFINIERT\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EDatenethik ist die Praxis, moralische Prinzipien darauf anzuwenden, wie Daten in Analytics, KI und Geschäftsabläufen erfasst, genutzt, geteilt und verwaltet werden.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_policy-overview_0":{"id":"text-002e7d6846","text":"\u003Cp\u003EDatenethik hilft Unternehmen, vertretbare Entscheidungen über die Datennutzung zu treffen, bevor diese fest in Pipelines, Modellen, Anwendungen und gemeinsam genutzten Datenprodukten verankert werden.\u003C/p\u003E\n\u003Cp\u003EUnternehmen legen Wert auf Datenethik, da die Art der Datennutzung heute Vertrauen, Risiken und die strategische Entscheidungsfindung beeinflusst. Kund:innen, Mitarbeitende, Aufsichtsbehörden und Geschäftspartner erwarten zunehmend, dass die Datennutzung jederzeit nachvollziehbar (explainable), verhältnismäßig und strikt auf den ursprünglichen Verwendungszweck ausgerichtet ist. \u003C/p\u003E\n\u003Cp\u003EDa KI- und ML-Systeme Datenentscheidungen zunehmend in automatisierte Ergebnisse umwandeln, können sich ethische Schwachstellen auf der Datenebene schnell vergrößern – insbesondere dann, wenn Trainingsdaten einen Bias aufweisen, sensible Attribute ungeprüft in Pipelines gelangen oder Daten über ihren genehmigten Zweck hinaus wiederverwendet werden.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-data-governance-policy":{"id":"title-v2-c42bc6f8d3","additionalClasses":"anchor-title anchor-title--what-is-a-data-ethics","type":"heading2","lines":["Was ist Datenethik?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-data-governance-policy_0":{"id":"text-9c3c1764e9","text":"\u003Cp\u003EDatenethik bezeichnet die Anwendung moralischer Prinzipien auf Entscheidungen darüber, wie Daten erfasst, gespeichert, genutzt und geteilt werden. Sie verleiht einem Governance-Programm seine fundamentale Werteebene und hilft Teams bei der Entscheidung, was nicht nur gesetzlich vorgeschrieben, sondern auch angemessen, verhältnismäßig und nachvollziehbar ist, wenn Daten durch Analytics-, KI- und Geschäfts-Workflows fließen. Durch die Übersetzung ethischer Verpflichtungen in Governance-Richtlinien und Plattformkontrollen können Unternehmen Daten sicherer nutzen und gleichzeitig das Risiko von Schäden verringern.\u003C/p\u003E\r\n\u003Cp\u003EIn der Praxis beginnt die Datenethik bereits weit vor dem Verfassen der eigentlichen Governance-Richtlinie. Eine Organisation legt zunächst fest, was sie mit Daten tun darf und was nicht. Dazu gehört, welche Nutzungen eine Einwilligung erfordern, welche sensiblen Attribute minimiert werden sollten, welche Datasets nicht ohne Überprüfung für das KI-Training wiederverwendet werden dürfen und welcher Audit-Trail existieren muss, wenn sich ein Datenprodukt auf Kund:innen, Mitarbeitende oder Patient:innen auswirkt. Diese Verpflichtungen müssen dann in Governance-Kontrollen überführt werden, wie z. B. Regeln für Erfassung, Zugriff, Aufbewahrung, Freigabe, Maskierung und Überprüfung.\u003C/p\u003E\r\n\u003Cp\u003EDer finale Schritt ist die Durchsetzung. Eine Richtlinie, die besagt, dass sensible demografische Attribute eingeschränkt werden sollten, ist effektiver, wenn die Datenplattform dabei helfen kann, diese Attribute zu identifizieren, passende Tags anzuwenden, Maskierung oder Zugriffsrichtlinien (Row Access Policies) zu unterstützen und Transparenz über nachgelagerte Nutzung und Zugriffsaktivitäten zu schaffen.\u003C/p\u003E\r\n\u003Cp\u003EDatenethik ist mit Datenschutz, Compliance und Governance verwandt, unterscheidet sich jedoch von diesen. \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDatenschutz\u003C/b\u003E konzentriert sich im Wesentlichen auf den Schutz personenbezogener Daten vor unbefugtem Zugriff oder Missbrauch. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ECompliance\u003C/b\u003E definiert rechtliche Verpflichtungen. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGovernance\u003C/b\u003E stellt die Rollen, Richtlinien und technischen Kontrollen bereit, um Daten über ihren gesamten Lebenszyklus hinweg sicher zu verwalten. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDatenethik\u003C/b\u003E prägt die Entscheidungen hinter diesen Mechanismen, einschließlich der Frage, welche Daten erfasst werden sollten, wer darauf zugreifen darf, wie lange sie aufbewahrt werden sollten und wann eine neue Nutzung eine zusätzliche menschliche Überprüfung erfordert.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EKI hat die ethischen Konsequenzen von Datenentscheidungen deutlich sichtbarer gemacht. Ein mit einem Bias behaftetes Trainings-Dataset kann Einstellungsempfehlungen, Kreditentscheidungen oder Triage-Workflows im Gesundheitswesen in großem Maßstab maßgeblich beeinflussen. Ein für einen bestimmten Zweck erfasstes Kundenattribut kann zu einer Eingabe für eine automatisierte Entscheidung werden. Eine Modell-Pipeline kann Daten auf eine Weise wiederverwenden, die technisch zulässig, aber ethisch schwer zu rechtfertigen ist. Datenethik hilft Organisationen dabei, genau solche Entscheidungen zu prüfen, bevor sie tief in Systeme eingebettet und schwer zu kontrollieren sind.\u003C/p\u003E\r\n\u003Cp\u003EDas EU-KI-Gesetz enthält \u003Ca href=\"https://snowflake.com/de/data-governance/\"\u003EData-Governance\u003C/a\u003E-Anforderungen für bestimmte Hochrisiko-KI-Systeme, einschließlich Praktiken im Zusammenhang mit Trainings-, Validierungs- und Test-Datasets, Datenerfassungsprozessen, Datenaufbereitung, möglichem Bias und dem ursprünglichen Zweck der Erfassung personenbezogener Daten. Das \u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\"\u003EAI Risk Management Framework des NIST\u003C/a\u003E verbindet zudem \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/\"\u003EKI-Governance\u003C/a\u003E durch Funktionen wie Steuern, Zuordnen, Messen und Verwalten mit organisatorischen Risikopraktiken.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EHören Sie sich den AI Data Cloud Podcast an, in dem Jack Berkowitz, Chief Data Officer bei ADP, \u003Ca href=\"https://www.snowflake.com/en/podcast/jack-berkowitz/\"\u003Eüber Data Sharing und die Anwendung von Datenethik auf Algorithmen spricht\u003C/a\u003E.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-a-data-governance-policy-matters":{"id":"title-v2-2f618bbbd6","additionalClasses":"anchor-title anchor-title--principles-data-ethics","type":"heading2","lines":["Prinzipien der Datenethik"],":type":"snowflake-site/components/title-v2"},"text_why-a-data-governance-policy-matters_0":{"id":"text-9620f96b91","text":"\u003Cp\u003EDatenethik-Programme variieren je nach Unternehmen, Branche und regulatorischem Umfeld, aber die meisten beruhen auf einem gemeinsamen Satz grundlegender Prinzipien. Diese Prinzipien helfen Datenteams, Stewards, Rechtsteams und Führungskräften einem gemeinsamen Satz grundlegender Prinzipien, konsistente Entscheidungen über die Datennutzung zu treffen, bevor diese fest in Pipelines, Modelle oder Applikationen integriert werden.\u003C/p\u003E\r\n\u003Ch3\u003EVerantwortungsvolle Datennutzung\u003C/h3\u003E\r\n\u003Cp\u003EEine verantwortungsvolle Datennutzung beschreibt das operative Bekenntnis, Daten so zu erfassen, zu speichern und zu analysieren, dass Risiken und Schäden minimiert, Rechte gewahrt und ausschließlich klar definierte Zwecke erfüllt werden. Sie verwandelt ethische Absichten in konkrete Entscheidungen, die direkt auf Pipeline-Ebene angewendet werden können: Welche Daten in einen Workflow einfließen, welche Felder beibehalten werden, welche Teams darauf zugreifen können und welche nachgelagerten Nutzungen zulässig sind.\u003C/p\u003E\r\n\u003Cp\u003EIm Mittelpunkt der verantwortungsvollen Datennutzung stehen in der Regel vier Verpflichtungen:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ERechtmäßige Erhebung: \u003C/b\u003EDaten werden auf Basis einer gültigen Rechtsgrundlage, mittels explicit Consent (ausdrücklicher Einwilligung) oder einer anderen freigegebenen Berechtigung erhoben.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EZweckbindung: \u003C/b\u003EDaten werden ausschließlich für den Zweck verwendet, der explizit angegeben, freigegeben oder vernünftigerweise vorausgesetzt wurde.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EVerhältnismäßigkeit: \u003C/b\u003ETeams sammeln und speichern nur die Daten, die für die spezifische Aufgabe benötigt werden.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESchadensminimierung: \u003C/b\u003EUnternehmen bewerten vorhersehbare nachgelagerte Schäden, bevor sie Datenprodukte, Analytics-Workflows oder KI-Systeme bereitstellen.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EDatenwürde\u003C/h3\u003E\r\n\u003Cp\u003EDatenwürde ist das übergeordnete Prinzip, dass Menschen in der Lage sein sollten, transparent zu verstehen und aktiv zu beeinflussen, wie Daten über sie genutzt werden. Sie betrachtet Daten als ein Element, das untrennbar mit der Autonomie und dem individuellen Kontext eines Menschen verbunden ist – und nicht bloß als ein reines wirtschaftliches Asset, das beliebig extrahiert, kombiniert und wiederverwendet werden kann.\u003C/p\u003E\r\n\u003Cp\u003EDies unterscheidet sich vom Datenschutz, auch wenn sich beide Bereiche überschneiden. Datenschutz hilft dabei, personenbezogene Daten vor unbefugtem Zugriff oder Missbrauch zu schützen. Die Datenwürde fragt danach, ob eine autorisierte Nutzung die Person, die durch die Daten repräsentiert wird, weiterhin respektiert. Beispielsweise können Patientenakten sicher gespeichert und von zugelassenen Nutzer:innen abgerufen werden. Die Verwendung dieser Akten zum Trainieren eines kommerziellen KI-Systems ohne ausreichende Kenntnis oder Zustimmung kann jedoch dennoch Bedenken hinsichtlich der Datenwürde aufwerfen.\u003C/p\u003E\r\n\u003Cp\u003EIn der Praxis beeinflusst die Datenwürde die Gestaltung der Einwilligung, die Zweckbindung, die Datenminimierung und die Rechte der betroffenen Personen. Die Zustimmung sollte transparent und aussagekräftig sein, anstatt tief in den allgemeinen Nutzungsbedingungen versteckt zu werden. Zweckangaben sollten spezifisch genug sein, um die zukünftige Datennutzung zu leiten. Die Datenerfassung sollte auf das beschränkt sein, was für die Aufgabe erforderlich ist. Zudem sollten betroffene Einzelpersonen über angemessene Möglichkeiten verfügen, um zu verstehen, anzufechten oder zu beeinflussen, wie ihre Daten verwendet werden, insbesondere wenn diese Nutzung den Zugang zu Diensten, Chancen oder Entscheidungen betrifft.\u003C/p\u003E\r\n\u003Ch3\u003ETransparenz und Open Data Governance\u003C/h3\u003E\r\n\u003Cp\u003ETransparenz bietet Personen innerhalb und außerhalb einer Organisation die Möglichkeit, zu verstehen, wie Daten konkret genutzt werden. Dies kann Lineage-Aufzeichnungen, Data Provenance, Katalog-Metadaten, Modelldokumentation, Governance-Genehmigungen und Audit-Protokolle umfassen. Das Ziel der Transparenz ist es, die richtigen Informationen für die richtigen Prüfenden, Stewards, Aufsichtsbehörden, Partner:innen oder Datenkonsument:innen sichtbar zu machen.\u003C/p\u003E\r\n\u003Cp\u003EOpen Data Governance wendet das Prinzip der Transparenz auf Daten an, die öffentlich zugänglich gemacht oder für Forschung, Rechenschaftspflicht (Accountability) oder Zusammenarbeit geteilt werden. Open Data kann Vertrauen, akademische Forschung und demokratische Rechenschaftspflicht fördern, aber uneingeschränkte Offenheit kann auch PII, proprietäre Geschäftslogik oder sensible Informationen des öffentlichen Sektors preisgeben. Ethische Open-Data-Programme balancieren eine barrierefreie Zugänglichkeit perfekt mit strikten Risk Controls aus. Die \u003Ca href=\"https://snowflake.com/de/data-governance/frameworks/fair-principles/\"\u003EFAIR-Prinzipien\u003C/a\u003E – auffindbar (findable), zugänglich (accessible), interoperabel (interoperable) und wiederverwendbar (resuable) – werden häufig als Leitfaden für verantwortungsvolle Open-Data-Praktiken herangezogen. \u003C/p\u003E\r\n\u003Cp\u003EIn der Praxis bedeutet dies die Nutzung von \u003Ca href=\"https://snowflake.com/de/data-governance/data-catalog/\"\u003EDatenkatalogen\u003C/a\u003E, um detaillierte Metadaten zu veröffentlichen, ohne sensible Felder preiszugeben, Governance-Audit-Protokolle für Aufsichtsbehörden zur Kontrolle zugänglich zu machen und versionierte Datasets mit klarer Herkunftsdokumentation zu pflegen, damit Nutzer:innen nachvollziehen können, wie Daten im Laufe der Zeit erstellt, transformiert und aktualisiert wurden.\u003C/p\u003E\r\n\u003Cp\u003EIn Deutschland hat die \u003Ca href=\"http://bmi.bund.de\"\u003EDatenethikkommission der Bundesregierung\u003C/a\u003E 2019 wegweisende Empfehlungen für den ethischen Umgang mit Daten und algorithmischen Systemen veröffentlicht. Sie definiert Prinzipien wie Transparenz, Nichtdiskriminierung und Verhältnismäßigkeit als Grundlage für eine verantwortungsvolle Datenwirtschaft – Werte, die sich direkt in einer Datenethik-Strategie eines Unternehmens widerspiegeln sollten. \u003C/p\u003E\r\n\u003Ch3\u003EFairness\u003C/h3\u003E\r\n\u003Cp\u003EFairness konzentriert sich darauf, ob datengestützte Systeme Ergebnisse erzeugen, die angemessen und vertretbar sind und geschützte oder gefährdete Gruppen nicht systematisch benachteiligen. In den Bereichen Analytics und KI hängt Fairness sowohl von der Datenbasis als auch von dem System ab, das sie verwendet.\u003C/p\u003E\r\n\u003Cp\u003EEin Einstellungsmodell, das beispielsweise auf Basis historischer Recruiting-Daten trainiert wurde, kann schädliche Muster aus der Vergangenheit reproduzieren, wenn die Trainingsdaten frühere Ausgrenzungen oder einen ungleichen Zugang zu Chancen widerspiegeln. Ebenso kann ein Triage-Modell im Gesundheitswesen bei verschiedenen Bevölkerungsgruppen völlig abweichende Outcomes liefern, wenn die zugrundeliegenden Daten bestimmte Gruppen unterrepräsentieren.\u003C/p\u003E\r\n\u003Cp\u003EFairness ist keine einzelne technische Einstellung – verschiedene Definitionen von Fairness können miteinander in Konflikt stehen. Zum Beispiel:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDemografische Parität\u003C/b\u003E (Demographic Parity) fragt danach, ob Ergebnisse gleichmäßig über verschiedene Gruppen hinweg verteilt sind.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EEqualized Odds\u003C/b\u003E konzentriert sich darauf, ob die Fehlerraten (Error Rates) über verschiedene Gruppen hinweg vergleichbar und ähnlich sind.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EIndividuelle Fairness\u003C/b\u003E fragt danach, ob ähnliche Individuen ähnlich behandelt werden. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ETeams müssen den Fairness-Standard wählen, der exakt zum jeweiligen Entscheidungskontext passt, und dokumentieren, warum diese Wahl angemessen ist.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_core-components-of-a-data-governance-policy":{"id":"title-v2-98d5ed7bcc","additionalClasses":"anchor-title anchor-title--data-ethics-risks-in-ai-and-analytics","type":"heading2","lines":["Datenethik-Risiken in KI und Analytics"],":type":"snowflake-site/components/title-v2"},"text_core-components-of-a-data-governance-policy_0":{"id":"text-b2c42013d5","text":"\u003Cp\u003EEthische Risiken treten häufig dann auf, wenn Daten von einem Kontext in einen völlig anderen Kontext übertragen werden. Diese Risiken sind besonders im Umfeld von KI und ML sichtbar, wo Datenentscheidungen die generierten Ergebnisse in großem Maßstab prägen können.\u003C/p\u003E\r\n\u003Ch3\u003EData Bias\u003C/h3\u003E\r\n\u003Cp\u003EData Bias beschreibt einen systematischen Fehler in einem Dataset, der dazu führen kann, dass Analytics- oder Modell-Outputs signifikant in eine bestimmte Richtung verzerrt werden. Ein Bias kann durch Stichprobenlücken (Sampling Gaps), historische Ungleichheiten, Messfehler, Labeling-Praktiken oder durch Geschäftsprozesse entstehen, die nie für den neuen Verwendungszweck konzipiert wurden.\u003C/p\u003E\r\n\u003Cp\u003EDies macht Bias zu einem Governance-Problem, nicht nur zu einem ML-Problem. Wenn ein:e Data Scientist ein Modell trainiert, sind möglicherweise bereits viele Bias-bezogene Entscheidungen in den Daten verankert: welche Populationen einbezogen wurden, welche Felder erfasst wurden, welche Labels angewendet wurden, welche Datensätze ausgeschlossen wurden und welche historischen Ergebnisse als Ground Truth behandelt wurden.\u003C/p\u003E\r\n\u003Cp\u003EDatenethik erfordert eine frühzeitige Überprüfung im Lebenszyklus. Teams müssen die genaue Herkunft der Daten (Data Provenance), den ursprünglichen Erhebungszweck, bekannte Repräsentationslücken sowie sämtliche Annahmen, die den vergebenen Labels oder den erzeugten Outcomes zugrunde liegen, tiefgehend verstehen. Im KI-Kontext deckt sich dies mit den regulatorischen Erwartungen und Risikomanagement hinsichtlich der Qualität von Trainingsdaten, der Repräsentativität und der Minderung von Bias (Bias Mitigation).\u003C/p\u003E\r\n\u003Ch3\u003EAlgorithmische Fairness\u003C/h3\u003E\r\n\u003Cp\u003EAlgorithmische Fairness konzentriert sich primär auf die generierten Model Outputs statt ausschließlich auf das Dataset allein. Sie hinterfragt, wie sich das Gesamtsystem verhält, sobald es diese Daten nutzt, um automatisierte Entscheidungen zu treffen oder zu unterstützen.\u003C/p\u003E\r\n\u003Cp\u003EPractitioner:innen bewerten die Fairness dabei oft an mehreren strategischen Checkpoints:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003EVor dem Training können sie die Datenzusammensetzung prüfen, um zu verstehen, ob relevante Bevölkerungsgruppen repräsentiert sind. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EWährend der Modellevaluierung können sie die Outputs gezielt auf einen potenziellen Disparate Impact (unverhältnismäßig negative Auswirkungen) nach einzelnen Kohorten testen. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EIn der Produktion können sie die Ergebnisse überwachen, um Drift, sich verändernde Fehlerraten oder unerwartete Diskrepanzen zu erkennen.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EIn Deutschland können Algorithmen, die diskriminierende Ergebnisse produzieren, gegen das Allgemeine Gleichbehandlungsgesetz (AGG) verstoßen – was algorithmische Fairness nicht nur zu einem ethischen Anliegen, sondern zu einer rechtlichen Anforderung macht.\u003C/p\u003E\r\n\u003Cp\u003EDas Entfernen eines sensiblen Feldes beseitigt nicht zwangsläufig das Risiko, da andere korrelierende Variablen im Datensatz unbemerkt als Proxys fungieren können. Beispielsweise nutzt ein Modell möglicherweise nicht direkt Faktoren wie ethnische Zugehörigkeit, Geschlecht, Behindertenstatus oder Einkommen, aber Standort, Bildungsweg (Education History), Kaufverhalten oder Beschäftigungsmuster können dennoch mit geschützten Attributen korrelieren. Ethische KI-Governance erfordert daher sowohl Kontrollen auf Datenebene als auch Überwachung auf Ausgabenebene.\u003C/p\u003E\r\n\u003Cp\u003EEntscheidungen zur algorithmischen Fairness erfordern ebenfalls eine Dokumentation. Wenn sich ein Team für ausgeglichene Chancen (Equalized Odds) statt für demografische Parität entscheidet, spiegelt diese Wahl Annahmen über den Entscheidungskontext, die akzeptablen Kompromisse sowie die spezifischen Risiken wider, welche die Organisation zu reduzieren versucht. Die Datenethik hilft dabei, sicherzustellen, dass solche weitreichenden Entscheidungen nicht intransparent in rein technischen Workflows verborgen bleiben.\u003C/p\u003E\r\n\u003Cp\u003EJennifer Belissent, Principal Data Strategist bei Snowflake, erklärt, wie verantwortungsvolle KI (Responsible AI) von einer soliden Datengrundlage abhängt: „Der Erfolg in der neuen KI-Landschaft hängt nicht nur von diesem glänzenden neuen Tool ab, sondern maßgeblich von den Fundamenten, auf denen es aufgebaut wird. Das Fundament für eine erfolgreiche und verantwortungsvolle Nutzung von KI und GenAI muss auf Datensicherheit, Datenvielfalt und organisatorischer Reife basieren.“\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_data_governanc":{"id":"quote-item-2522795ea9","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist bei Snowflake","showQuoteIcon":true,"quote":" Die Grundlage für den erfolgreichen und verantwortungsvollen Einsatz von KI und generativer KI muss auf Datensicherheit, Datendiversität und organisatorischer Reife basieren","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_966706563":{"id":"text-12d4025c48","text":"\u003Ch3\u003EVerzerrte oder würdeverletzende Modellausgaben\u003C/h3\u003E\n\u003Cp\u003ERisiken in Bezug auf Bias und Datenwürde können zusammentreffen, wenn sensible Daten ohne ausreichende Prüfung in operative KI-Workflows einfließen. Ein Modell kann Ausgaben generieren, die eine bestimmte Gruppe benachteiligen, geschützte Informationen preisgeben, die eigentlich strikt hätten minimiert werden müssen, oder personenbezogene Daten auf eine Weise verwenden, die nicht dem ursprünglichen Erhebungszweck entspricht.\u003C/p\u003E\n\u003Cp\u003EGenau aus diesem Grund sind automatisierte Governance-Kontrollen so wichtig Gezielte Row-level Access Policies, umfassende Masking Policies und eindeutige Object Tags können entscheidend dabei helfen, zu kontrollieren, welche demografischen, gesundheitlichen, finanziellen oder verhaltensbasierten Attribute überhaupt in die sensiblen Model-Training-Pipelines gelangen. Die Datenherkunft kann Teams dabei helfen, nachzuverfolgen, ob ein sensibles Feld aus einer verwalteten Quelle in eine abgeleitete Tabelle, ein Feature-Set oder eine Anwendung verschoben wurde. Die Access History kann transparent aufzeigen, welche Nutzer:innen wann welches Dataset abgefragt haben.\u003C/p\u003E\n\u003Cp\u003EKontrollen lösen nicht jede ethische Frage, aber sie schaffen die Voraussetzungen für Überprüfung, Durchsetzung und Verantwortlichkeit. Ohne sie hängt die Datenethik an jedem einzelnen Handoff-Punkt komplett vom individuellen Urteilsvermögen ab. Mit ihnen können ethische Grundsätze direkt in automatisierte, wiederholbare Governance-Regeln übersetzt werden.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy":{"id":"text-8f499eef15","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EHÄUFIGE FALLE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EViele Organizations investieren in das Tagging sensibler Daten und die Definition von Klassifizierungen, versäumen es dann jedoch, diese Tags mit Zugriffskontrollen, Maskierungs-, Aufbewahrungs- und Überprüfungs-Workflows zu verknüpfen. Infolgedessen werden Daten zwar richtig gekennzeichnet, aber dennoch falsch gehandhabt.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-legal-privacy-and-usage-requirements-shape-governance-policy":{"id":"title-v2-f2a3e91574","additionalClasses":"anchor-title anchor-title--how-organizations-operationalize-data-ethics","type":"heading2","lines":["Wie Organisationen Datenethik erfolgreich operationalisieren"],":type":"snowflake-site/components/title-v2"},"text_303662111":{"id":"text-43e890be32","text":"\u003Cp\u003EDatenethik muss die Workflows erreichen, in denen Daten erfasst, abgefragt, geteilt und wiederverwendet werden. In der Praxis operationalisieren Unternehmen dies durch eine Reihe miteinander verzahnter Praktiken.\u003C/p\u003E\n\u003Ch3\u003EWerte-Zusagen dokumentieren\u003C/h3\u003E\n\u003Cp\u003EOrganisationen beginnen typischerweise damit, verbindlich festzulegen, was sie mit Daten tun dürfen und was nicht. Diese Zusagen sollten spezifisch genug sein, um als verlässlicher Leitfaden für anstehende Entscheidungen zu dienen. Eine pauschale Aussage, dass die Organisation Daten verantwortungsvoll nutzt, ist weitaus weniger nützlich als eine konkrete, klare Zusage – wie etwa zur Minimierung der Collection sensibler Daten, zur Vermeidung einer Secondary Use (Zweitnutzung) ohne vorheriges Review oder zur lückenlosen Dokumentation von Fairness-Entscheidungen für Automated Decision Systems.\u003C/p\u003E\n\u003Cp\u003EDiese Zusagen brauchen auch Eigentümer (Oweners). Ein gelebtes Data Stewardship überträgt fest benannten Verantwortlichen die klare Verantwortung für spezifische Domänen, Definitionen, Datenqualität, Data Access und die strikte Policy Adherence (Einhaltung von Richtlinien). Rechts-, Compliance-, Security- und Business-Teams können bei der Definition der Verpflichtungen helfen; Data Stewards helfen jedoch aktiv dabei, diese auf tatsächliche Tabellen, Felder, Pipelines und Data Products anzuwenden.\u003C/p\u003E\n\u003Ch3\u003EWerte-Zusagen in Governance-Richtlinien verankern\u003C/h3\u003E\n\u003Cp\u003ESobald die Zusagen definiert sind, benötigen Unternehmen Governance-Richtlinien, die präzise festlegen, was geschehen muss. Eine Verpflichtung zur Datenminimierung kann zu einer Aufbewahrungsrichtlinie werden, die Datensätze nach einem bestimmten Zeitraum löscht oder archiviert. Eine Zusage zur Wahrung der Datenwürde könnte zu einem standardisierten Consent-Review-Prozess für neue Verwendungsarten personenbezogener Daten werden. Eine Verpflichtung zur Fairness erfordert möglicherweise umfassende Audits der Datenzusammenstellung vor der Modellbereitstellung (Model Deployment).\u003C/p\u003E\n\u003Cp\u003ERichtlinien sollten eine direkte Verbindung zum Datenlebenszyklus herstellen. Datenerfassungsrichtlinien definieren, welche Daten überhaupt erfasst werden können und auf welcher rechtlichen oder ethischen Grundlage dies geschieht. Zugriffsrichtlinien definieren, wer sensible Datenfelder nutzen oder darauf zugreifen kann. Aufbewahrungsrichtlinien legen fest, wie lange Daten maximal verfügbar bleiben können. Richtlinien für die Freigabe definieren, wann Daten veröffentlicht, ausgetauscht oder Partner:innen zur Verfügung gestellt werden können. Prüfrichtlinien (Review Policies) definieren, wann eine neue Verwendung genehmigt werden muss.\u003C/p\u003E\n\u003Ch3\u003EPolicies über automatisierte Platform-Controls durchsetzen\u003C/h3\u003E\n\u003Cp\u003EEthische Policies lassen sich auf Dauer nur schwer aufrechterhalten, wenn sie ausschließlich von manueller Überprüfung abhängig sind. Plattformkontrollen helfen bei der Durchsetzung von Richtlinien, bei denen Daten gespeichert, abgefragt, freigegeben und genutzt werden.\u003C/p\u003E\n\u003Cp\u003EGezielte Maskierungsrichtlinien können die Belichtung sensibler Tabellenspalten reduzieren. Row-level Access Policies können präzise einschränken, welche spezifischen Records (Datensätze) einzelne Nutzer:innen oder Rollen einsehen können. Object Tags können sensible Daten, freigegebene Verwendungszwecke, die Domain Ownership, gesetzliche Aufbewahrungsfristen (Retention) oder den aktuellen Klassifizierungsstatus automatisch kennzeichnen. Eine automatisierte Datenklassifizierung kann dabei helfen, potenziell sensible Daten frühzeitig zu identifizieren, sodass sie einheitlich kontrolliert werden können. \u003C/p\u003E\n\u003Cp\u003EEine Richtlinie könnte beispielsweise vorsehen, dass sensible demografische Attribute für das Modelltraining nicht allgemein verfügbar sein sollten. Eine integrierte Platform-Control kann diese Attribute zuverlässig maskieren, den Zugriff strikt auf freigegebene Rollen einschränken und sämtliche Metadata lückenlos bewahren, die aufzeigen, wie die Daten kontrolliert werden.\u003C/p\u003E\n\u003Ch3\u003EData Lineage und Audit Trails zum Nachweis einer verantwortungsvollen Datennutzung nutzen\u003C/h3\u003E\n\u003Cp\u003EOrganisationen benötigen für Compliance-Zwecke auch stets belastbare Nachweise (Evidence). Eine lückenlose Data Lineage hilft entscheidend dabei, transparent aufzuzeigen, woher Daten ursprünglich stammen, wie sie transformiert wurden und welche Downstream-Assets direkt von ihnen abhängen. Audit Trails helfen entscheidend dabei, transparent aufzuzeigen, welche Nutzer:innen wann und unter welchem genauen Kontext auf Daten zugegriffen haben. \u003C/p\u003E\n\u003Cp\u003EZusammen helfen sie Teams dabei, rechtssicher nachzuweisen, dass Daten ausschließlich für einen definierten Zweck genutzt wurden und governed Fields nicht unbemerkt in unautorisierte Workflows einfließen konnten. Diese Nachweise (Evidence) stützen sowohl die interne Accountability (Rechenschaftspflicht) als auch die externe regulatorische Aufsicht.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy":{"id":"text-3277c397dd","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EWICHTIGSTE ERKENNTNIS\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EDie zuverlässigste Methode zur Operationalisierung der Datenethik besteht darin, Klassifizierung und Absicht mit der Durchsetzung und Überprüfung zu verbinden: Markieren Sie sensible Daten, binden Sie diese Tags an Zugriffs-, Maskierungs- und Aufbewahrungsrichtlinien und verwenden Sie Lineage- und Auditpfade, um zu verifizieren, wie diese Daten tatsächlich verwendet werden, während sie in Analytics- und KI-Workflows übertragen werden.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_how-legal-privacy-and-usage-requirements-shape-governance-policy_0":{"id":"text-7886476514","text":"\u003Ch3\u003EDatennutzung bei sich veränderndem Kontext überprüfen\u003C/h3\u003E\n\u003Cp\u003EDatenflüsse verändern sich, Business-Anforderungen wandeln sich kontinuierlich und moderne AI-Systeme schaffen ständig neue Formen der Datenwiederverwendung. Was bei der Datenerfassung risikoarm war, kann schnell sensibel werden, wenn es gefügt, umgeschult oder in großem Umfang in die Produktion überführt wird.\u003C/p\u003E\n\u003Cp\u003EKontinuierliche Überprüfung hilft Unternehmen dabei, Richtlinien stets präzise an veränderte Kontexte anzupassen. Dazu können regelmäßige Zugriffsprüfungen, Aufbewahrungsprüfungen, Datenproduktzertifizierung, Fairness-Audits, Lineage-Prüfungen und Genehmigungsworkflows für neue Anwendungsfälle gehören. Die menschliche Überwachung (Human Oversight) bleibt unverzichtbar, da ethische Fragen oft einen Kontext beinhalten, der in einer Regel nicht vollständig erfasst werden kann.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2":{"id":"card-v2-7947cdf952","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EMerkle konnte seinen Datenentwicklungszyklus um 64% verbessern und durch die Konsolidierung sensibler Workflows für Marketingdaten, Governance und Kollaboration in Snowflake bis zu 20% an Kosteneinsparungen erzielen (Stand: März 2025). Das Team entwickelte außerdem eine RFP-Reaktionslösung mit Document AI in Snowflake Cortex, die die Dateneingabe für mindestens 25 Teammitglieder reduzierte.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--37461af2-131c-4f09-a134-dc5d00f88e7d/merkle-switzerland-ag%25403x.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"merkle customer logo","height":"351","width":"624",":type":"snowflake-site/components/image"},"layoutStyle":"horizontal","type":"content-card",":type":"snowflake-site/components/card-v2","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/en/customers/all-customers/case-study/merkle/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Customer Story lesen"},"title":{"id":"title","type":"heading4","lines":["Customer Story: Merkle "],":type":"snowflake-site/components/title-v2"}},"title_operational-policy-areas":{"id":"title-v2-0230216ff6","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-data-ethics","type":"heading2","lines":["Wie Snowflake Datenethik unterstützt"],":type":"snowflake-site/components/title-v2"},"text_operational-policy-areas_0":{"id":"text-cada5b5976","text":"\u003Cp\u003ESnowflake unterstützt Unternehmen bei der Operationalisierung der Datenethik, indem es Governance-Richtlinien, Metadaten und automatisierte Kontrollen innerhalb der gesamten Datenumgebung intelligent miteinander verknüpft. \u003C/p\u003E\r\n\u003Ch3\u003EGovernance-Kontext zentralisieren\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E unterstützt Teams dabei, Daten, Apps und Modelle in der gesamten AI Data Cloud zu entdecken, zu verstehen und zu kontrollieren. 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oft als Reaktion auf gesetzliche Anforderungen wie die \u003Ca href=\"https://gdpr.eu/what-is-gdpr/\" target=\"_blank\"\u003EDSGVO (GDPR)\u003C/a\u003E oder den \u003Ca href=\"https://oag.ca.gov/privacy/ccpa\" target=\"_blank\"\u003ECalifornia Consumer Privacy Act\u003C/a\u003E. 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