Now showing 1 - 10 of 12
  • Publication
    The why and how of trustworthy AI
    Artificial intelligence is increasingly penetrating industrial applications as well as areas that affect our daily lives. As a consequence, there is a need for criteria to validate whether the quality of AI applications is sufficient for their intended use. Both in the academic community and societal debate, an agreement has emerged under the term “trustworthiness” as the set of essential quality requirements that should be placed on an AI application. At the same time, the question of how these quality requirements can be operationalized is to a large extent still open. In this paper, we consider trustworthy AI from two perspectives: the product and organizational perspective. For the former, we present an AI-specific risk analysis and outline how verifiable arguments for the trustworthiness of an AI application can be developed. For the second perspective, we explore how an AI management system can be employed to assure the trustworthiness of an organization with respect to its handling of AI. Finally, we argue that in order to achieve AI trustworthiness, coordinated measures from both product and organizational perspectives are required.
  • Publication
    Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning
    Machine learning and artificial intelligence have become crucial factors for the competitiveness of individual companies and entire economies. Yet their successful deployment requires access to a large volume of training data often not even available to the largest corporations. The rise of trustworthy federated digital ecosystems will significantly improve data availability for all participants and thus will allow a quantum leap for the widespread adoption of artificial intelligence at all scales of companies and in all sectors of the economy. In this chapter, we will explain how AI systems are built with data science and machine learning principles and describe how this leads to AI platforms. We will detail the principles of distributed learning which represents a perfect match with the principles of distributed data ecosystems and discuss how trust, as a central value proposition of modern ecosystems, carries over to creating trustworthy AI systems.
  • Publication
    Constructing Spaces and Times for Tactical Analysis in Football
    ( 2021)
    Andrienko, Gennady
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    Andrienko, Natalia
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    Anzer, Gabriel
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    Bauer, P.
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    Budziak, G.
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    Weber, H.
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    A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts.
  • Publication
    Trustworthy Use of Artificial Intelligence
    ( 2019-07)
    Cremers, Armin B.
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    Englander, Alex
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    Gabriel, Markus
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    Rostalski, Frauke
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    Sicking, Joachim
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    Volmer, Julia
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    Voosholz, Jan
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    This publication forms a basis for the interdisciplinary development of a certification system for artificial intelligence. In view of the rapid development of artificial intelligence with disruptive and lasting consequences for the economy, society, and everyday life, it highlights the resulting challenges that can be tackled only through interdisciplinary dialog between IT, law, philosophy, and ethics. As a result of this interdisciplinary exchange, it also defines six AI-specific audit areas for trustworthy use of artificial intelligence. They comprise fairness, transparency, autonomy and control, data protection as well as security and reliability while addressing ethical and legal requirements. The latter are further substantiated with the aim of operationalizability.
  • Publication
    Max-Sum Dispersion via Quantum Annealing
    We devise an Ising model for the max-sum dispersion problem which occurs in contexts such as Web search or text summarization. Given this Ising model, max-sum dispersion can be solved on adiabatic quantum computers; in proof of concept simulations, we solve the corresponding Schrödinger equations and observe our approach to work well.
  • Publication
    Vertrauenswürdiger Einsatz von Künstlicher Intelligenz
    (Fraunhofer IAIS, 2019)
    Cremers, Armin B.
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    Englander, Alex
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    Gabriel, Markus
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    Rostalski, Frauke
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    Sicking, Joachim
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    Voosholz, Jan
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    Die 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.
  • Publication
    A QUBO Formulation of the k-Medoids Problem
    We are concerned with k-medoids clustering and propose aquadratic unconstrained binary optimization (QUBO) formulation of the problem of identifying k medoids among n data points without having to cluster the data. Given our QUBO formulation of this NP-hard problem, it should be possible to solve it on adiabatic quantum computers.
  • Publication
    Fraunhofer Big Data and Artificial Intelligence Alliance
    Big data is a management issue across sectors and promises to deliver a competitive advantage via structured knowledge, increased efficiency and value creation. Within companies, there is significant demand for big data skills, individual business models, and technological solutions. Fraunhofer assists companies to identify and mine their valuable data. Experts from Fraunhofers Big Data and Artificial Intelligence Alliance demonstrate how companies can benefit from an intelligent enrichment and analysis of their data.
  • Publication
    Fraunhofer-Allianz Big Data
    Big Data ist branchenübergreifend ein Management-Thema und verspricht der Wirtschaft Vorsprung durch strukturiertes Wissen, mehr Effizienz und Wertschöpfung. In den Unternehmen gibt es einen hohen Bedarf an Big-Data- Kompetenzen, individuellen Geschäftsmodellen und technischen Lösungen. Fraunhofer unterstützt Unternehmen dabei, ihre Datenschätze zu identifizieren und zu heben. Experten der Fraunhofer-Allianz Big Data zeigen auf, wie Unternehmen von der intelligenten Anreicherung und Analyse ihrer Daten profitieren können.