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Department of Social Sciences

SFS presentation on "Trustworthy AI" at the 2nd World Conference on Data Science & Statistics

© Thorben Krokowski​/​sfs

At the 2nd World Conference on Data Science & Statistics from June 17-19 in Amsterdam, around 100 industry leaders, innovators and academics from data science, big data, ML, AI, IoT, analytics and social sciences met to discuss the interaction between data science, AI and other technologies and their impact on data, people and processes in 60 presentations.

In his lecture entitled "Trustworthy AI as a German-European distinction trait?", Thorben Krokowski presented some of the results of his joint research work with Prof. Dr. Hartmut Hirsch-Kreinsen over the past few years. In their research, the two academics are addressing, among other things, concerns about the ethical and social consequences of the unregulated development and, above all, use of AI systems in a variety of areas of society.

It is undeniable that the application of AI requires social standardization and regulation. For years, innovation policy measures and a wide range of activities by European and German institutions have been geared towards this goal. Under the label "Trustworthy AI" (TAI), a promise is formulated according to which AI can fulfill criteria of transparency, legality, privacy, non-discrimination and reliability.

In his presentation, Thorben Krokowski focused on the question of what significance and scope the politically initiated concepts of TAI have in the current process of AI dynamics and to what extent they can stand for an independent, unique European or German development path for this technology. In addition, key aspects were presented which, according to the scientists, must be improved for the development of trustworthy AI and which, in addition to the need to accelerate cross-border cooperation in the field of AI in order to achieve joint progress, manifest themselves in the great importance of establishing and consolidating a common European idea to support trustworthy AI as well as identifying industrial niches and reflecting on existing strengths instead of obsessively chasing a supposedly innovative-revolutionary AI path.

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© Thorben Krokowski​/​sfs