Socially Assistive Robots are increasingly conceived as applicable tools to be used in aged care. However, the use carries many negative and positive connotations. Negative connotations come forth out of romanticized views of care practices, disregarding their already established technological nature. Positive connotations are formulated out of techno-deterministic views on SAR use, presenting it as an inevitable and necessary next step in technological development to guarantee aged care. Ethical guidance of SAR use inspired by negative connotations tends to be over-restrictive (...) whereas positive connotations tend to provide over-permissive guidance. To avoid these extremes, we report on the development and content of 21 ethical orientations regarding SAR use in aged care. These orientations resulted from a multi-phased project, which consisted of empirical-ethical research focusing on older adults’ intuitions regarding SAR use and philosophical-ethical research focusing on philosophical-ethical argumentations regarding SAR use. This project led to the Socio-historical contextualization of the ethics of SAR use, in which the ethical impact of SAR use is localized on three interrelated analysis levels: societal, organizational, and individual-relational. The 21 novel orientations regarding SAR use are structured according to these levels and further categorized into foundational and applied orientations. The first category leads to critical reflection on SAR use while the latter category inspires decision-making processes regarding this use. While going beyond the care-romantic and techno-deterministic gaze of SAR use in aged care, the described orientations balance themselves between their over-restrictiveness and over-permissiveness. (shrink)

Science is being transformed by the increasing capabilities of automation technologies and artificial intelligence (AI). Integrating AI and machine learning (ML) into scientific practice requires changing established research methods while maintaining a scientific understanding of research findings. Researchers are at the forefront of this change, but there is currently little understanding of how they are experiencing these upheavals in scientific practice. In this paper, we examine how researchers working in several research fields (automation engineering, computational design, conservation decision-making, materials science, (...) and synthetic biology) perceive AI/ML technologies used in their work, such as laboratory automation, automated design of experiments, computational design, and computer experiments. We find that researchers emphasised the need for AI/ML technologies to have practical benefits (such as efficiency and improved safety) to justify their use. Researchers were also hesitant to automate data analysis, and the importance of explainability differed between researchers working with laboratory automation and those using AI/ML directly in their research. This difference is due to the different role AI/ML plays in different research fields: laboratory automation performs processes already defined by the researcher and the actions are visible or recorded, while in AI/ML applications the decisions that produced the result may be obscure to the researcher. Understanding the role AI/ML plays in scientific practice is important for ensuring that scientific knowledge continues to grow. (shrink)