on-site: seminar room 403
digital: link via E-Mail ([email protected])
Gerhard Schreiber (HSU): Data Toxicality: Observations and Reflections from a Techno-Ethical Perspective
In biochemistry, “toxic” refers to the harmful effects of a substance when it comes into contact with a biological system. Extending this scientific concept, “toxic” has increasingly been applied to destructive patterns of thought and behavior that harm social cohesion and interpersonal relationships, as seen in terms such as “toxic masculinity.” The term has been further expanded to include anything associated with risk, dysfunction, or negative outcomes, including “toxic assets,” “toxic relationships,” and even “toxic positivity.”
This presentation introduces the term “data toxicality” as part of the ongoing expansion of “toxicity,” while offering a distinct focus. Specifically, it emphasizes the social-psychological dimensions of harm that occur in interpersonal relationships and social interactions. Unlike the established understanding of toxicity in pharmacological, biochemical, genetic, physical, or physiological contexts, “data toxicality” aims to capture the potential harm that data can inflict on human coexistence. By examining examples and proposing techno-ethical frameworks, the presentation seeks to illuminate pathways for addressing and mitigating the socio-psychological harms of data in the digital age.
Kathrin Welker (HSU): Constrained shape optimization in shape spaces: Models raising from deterministic to stochastic settings
Shape optimization is concerned with identifying shapes (or subsets of R2) behaving in an optimal way with respect to a given physical system. It has been an active field of research for the past decades and is used for example in (civil) engineering. Many relevant problems in the area of shape optimization involve a constraint in the form of a partial differential equation (PDE). Theory and algorithms in shape optimization can be based on techniques from differential geometry, e.g., a Riemannian manifold structure can be used to define the distances of two shapes. Thus, shape spaces are of particular interest in shape optimization. In this talk, we apply the differential-geometric structure of Riemannian shape spaces to the theory of classical PDE constrained shape optimization problems. We propose for example a space containing shapes in R2 admitting piecewise-smooth curves as elements. Since many relevant problems in the area of shape optimization involve a constraint in the form of a partial differential equation, which contains inputs or material properties that may be unknown or subject to uncertainty, we consider also stochastic shape optimization problems. We present algorithms to solve deterministic and stochastic PDE constrained (multi-)shape optimization problems and give numerical results of these algorithms.