Ethics of Autonomous Experimental Systems: Understanding Experts' Views on Automating Scientific Discovery

Automation plays a crucial role in scientific research. Advanced computing techniques and algorithm optimization are regarded as tools for enhancing data collection and analysis, and to improve replicability of experiments then resulting in accelerating the discovery of chemicals and materials. However, the decision-making process related to the automatization of tasks in the lab is influenced not solely by scientific objectivity considerations but also by socio-economic, cultural, ethical, and political factors. This article presents the results of an empirical sociological inquiry about scientists' attitudes towards automation in bioengineering and materials sciences. The study employed thematic analysis of interviews conducted with scientists in from research laboratories in the New York Metropolitan area. The findings reveal some factors affecting experts' decision making on automating research on bio and nano-scale engineering and materials discovery: scientist’s experience in the field; reflections on the cost-benefit of automation investments; relevance of human validation of experiments; collective trust in autonomously generated molecules and materials by peers; and communicational issues experienced between researchers holding multiple disciplinary affiliations. We argue that, in areas of disruptive innovation such as bioengineering and materials sciences, the decision to automate research advances through tensions arising from the sociocultural environment, which supports the co-production of novel arenas of research and its technopolitical infrastructures. This is a dynamic process that encompass both technical and non-technical arguments, prioritizing mechanisms to continuously support narratives on the objective nature of scientific methods and practices. This resonates with current debates on the overestimated relevance of computational tools such as AI and other algorithm optimization technologies in innovative research fields. It indicates the need to critically examine how claims of scientific objectivity and technical interventions balance each other to stabilize the social order of emerging science and technology.