Auflistung nach Autor:in "Rebstadt,Jonas"
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- TextdokumentLearning without Looking: Similarity Preserving Hashing and Its Potential for Machine Learning in Privacy Critical Domains(INFORMATIK 2022, 2022) Eleks,Marian; Rebstadt,Jonas; Fukas,Philipp; Thomas,OliverMachine Learning is frequently ranked as one of the most promising technologies in several application domains but falls short when the data necessary for training is privacy-sensitive and can thus not be used. We address this problem by extending the field of Privacy Aware Machine Learning with the application of Similarity Preserving Hashing algorithms to the task of data anonymization in a Design Science Research approach. In this endeavor, novel anonymization algorithms made to enable Machine Learning on anonymized data are designed, implemented, and evaluated. Throughout the Design Science Research process, we present a collection of issues and requirements for Privacy Aware Machine Learning algorithms along with three Similarity Preserving Hashing-based algorithms to fulfil them. A metric-based comparison of established and novel algorithms as well as new arising opportunities for Machine Learning on sensitive data are also added to the current knowledge base of Information Systems research.
- TextdokumentTowards the Operationalization of Trustworthy AI: Integrating the EU Assessment List into a Procedure Model for the Development and Operation of AI-Systems(INFORMATIK 2022, 2022) Kortum,Henrik; Rebstadt,Jonas; Böschen,Tula; Meier,Pascal; Thomas,OliverArtificial intelligence (AI) is increasingly permeating all areas of life and not only changing coexistence in society for the better. Unfortunately, there is an increasing number of examples where AI systems show problematic behavior, such as discrimination or insufficient accuracy, missing data privacy or transparency. To counteract this trend, an EU initiative has drafted a legal framework and recommendations on how AI can be more trustworthy and comply with people's fundamental rights. However, fundamental rights are currently not reflected in procedure models for the development and operation of AI systems. Our work contributes to closing this gap so that companies, especially SMEs with small IT departments and limited financial resources, are supported in the development process. Within the framework of a structured literature review, we derive a procedure model for the development and operation of AI systems and subsequently integrate concrete recommendations for achieving trustworthiness.