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Prof. Dr.
Wrobel, Stefan
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PublicationGuideline for Designing Trustworthy Artificial Intelligence(Fraunhofer IAIS, 2023-02)
;Cremers, Armin B. ;Houben, Sebastian ;Sicking, Joachim ;Loh, Silke ;Stolberg, EvelynTomala, Annette DariaArtificial Intelligence (AI) has made impressive progress in recent years and represents a a crucial impact on the economy and society. Prominent use cases include applications in medical diagnostics,key technology that has predictive maintenance and, in the future, autonomous driving. However, it is clear that AI and business models based on it can only reach their full potential if AI applications are developed according to high quality standards and are effectively protected against new AI risks. For instance, AI bears the risk of unfair treatment of individuals when processing personal data e.g., to support credit lending or staff recruitment decisions. Serious false predictions resulting from minor disturbances in the input data are another example - for instance, when pedestrians are not detected by an autonomous vehicle due to image noise. The emergence of these new risks is closely linked to the fact that the process for developing AI applications, particularly those based on Machine Learning (ML), strongly differs from that of conventional software. This is because the behavior of AI applications is essentially learned from large volumes of data and is not predetermined by fixed programmed rules. -
PublicationLeitfaden zur Gestaltung vertrauenswürdiger Künstlicher Intelligenz (KI-Prüfkatalog)(Fraunhofer IAIS, 2021)
;Cremers, Armin B. ;Sicking, Joachim -
PublicationZertifizierung von KI-Systemen. Kompass für die Entwicklung und Anwendung vertrauenswürdiger KI-Systeme( 2020)
;Heesen, Jessica ;Müller-Quade, Jörn -
PublicationVertrauenswürdiger Einsatz von Künstlicher Intelligenz(Fraunhofer IAIS, 2019)
;Cremers, Armin B. ;Englander, Alex ;Gabriel, Markus ;Rostalski, Frauke ;Sicking, Joachim ;Voosholz, JanDie vorliegende Publikation dient als Grundlage für die interdisziplinäre Entwicklung einer Zertifizierung von Künstlicher Intelligenz. Angesichts der rasanten Entwicklung von Künstlicher Intelligenz mit disruptiven und nachhaltigen Folgen für Wirtschaft, Gesellschaft und Alltagsleben verdeutlicht sie, dass sich die hieraus ergebenden Herausforderungen nur im interdisziplinären Dialog von Informatik, Rechtswissenschaften, Philosophie und Ethik bewältigen lassen. Als Ergebnis dieses interdisziplinären Austauschs definiert sie zudem sechs KI-spezifische Handlungsfelder für den vertrauensvollen Einsatz von Künstlicher Intelligenz: Sie umfassen Fairness, Transparenz, Autonomie und Kontrolle, Datenschutz sowie Sicherheit und Verlässlichkeit und adressieren dabei ethische und rechtliche Anforderungen. Letztere werden mit dem Ziel der Operationalisierbarkeit weiter konkretisiert. -
PublicationOptimistic estimate pruning strategies for fast exhaustive subgroup discoverySubgroup discovery is the task of finding subgroups of a population which exhibit both distributional unusualness and high generality at the same time. Since the corresponding evaluation functions are not monotonic, the standard pruning techniques from monotonic problems such as frequent set discovery cannot be used. In this paper, we show that optimistic estimate pruning, previously considered only in a very simple and heuristic way, can be developed into a sound and highly effective pruning approach for subgroup discovery. We present and prove new optimistic estimates for several commonly used subgroup quality functions, describe a subgroup discovery algorithm with novel exploration strategies based on optimistic estimates, and show that this algorithm significantly outperforms previous algorithms by a wide margin of an order of magnitude or more.