Minisymposium

The increasing quantities of data and rapid advances in AI have sparked discussions around ethical considerations of computational research and societal ramifications of technology applied to every aspect of our lives. What about High Performance Computing? Is the use of HPC neutral in the contexts of fairness, societal impact and accessibility? What does that imply for HPC education and training? How should we educate the current and future generations of computational scientists and Research Software Engineers to maximise benefits of HPC and computational science for individuals and society and minimise risk and harm? How should we teach research ethics and integrity when we struggle to define what they mean for HPC?

As educators, can we increase awareness of the costs and benefits of HPC? What can we teach about effective use of resources and minimising the impact of research utilising HPC on the environment? What should be done to make HPC more accessible and can we make it a viable career option for underrepresented groups? Will AI be democratising or polarising for HPC, and how do we teach appropriate use? The goal of this presentation is to start a discussion and gather community feedback to start outlining best practices.

Recent advances in high performance computing (HPC) and artificial intelligence (AI) have reignited discussions for more responsible and ethical computing within the context of cultures, sociotechnical ecosystems, and evolving societal norms. This talk provides practical guidelines that scientists, educators, and practitioners alike can employ to become more aware of personal values that may unconsciously shape approaches to computation, design, and ethics. Emphasis of this talk will be placed on honing individual ethical mindsets and its application to scientific and research software teams.

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.

Programming scientific computing systems is a complex job constantly under labor pressures. Financial incentives from industry and potential work visa and export control restrictions make employment in this sector even more difficult. With the appearance of LLM-based code generators like CoPilot, several ethical concerns arise. First, how are these models trained and created? The training data comes from potentially illegally used materials, such as open source code that requires attribution, but none is generated. Second, the correctness of these systems requires higher skills to validate the code generated does exactly and only what the requestor wants. Third, security flaws introduced by poisoning the source pool on repositories like Github make relying on the code safety questionable.

This talk will delve into some of the ethical problems with these code generators and whether or not it is ethical to use them, trust or not, to achieve the advancement of scientific inquiry. The chronic labor shortages encourage using these tools as a short cut, but is that wise, ethical, or even useful?