Minisymposium Presentation

Scientific computing and high-performance computing (HPC) increasingly directly impact society. This can be observed by the growing number of disciplines and domain sciences relying on access to large-scale computing and data facilities to conduct their research. In particular machine learning technologies augment the capabilities to automate and scale decision making to unprecedented scales. As a result ethical considerations surface at the level of applications both in their implementation and deployment. Most prominently the impact of machine learning approaches on society and the growing energy footprint of large-scale data centers gained public attention but they are not limited to machine learning and artificial intelligence (AI).As practitioners enabling the use of these technologies, we need to be prepared and educated on ethical principles but also the context of applications with respect to, for example, infrastructure and energy. This work presents various such perspectives aiming to look beyond the spotlight and offer different contextual and an evolution of different applications and technologies and their characteristics and distribution. The work critically discusses also the limitations of the perspectives and highlights current blindspots that need to be addressed to better back up ethical considerations in HPC and scientific computing with data.