Ethics and Accountability of Care Robots


  • Jyri Rajamäki Laurea University of Applied Sciences
  • Jaakko Helin



care robot, ethics, accountability


The subject of this paper is ethical and responsibility issues relating to the development and acquisition of robotics in healthcare. The purpose of the paper is to study previous scientific publications and research related to the topic and to clarify which questions, aspects, and concerns are most relevant when considering ethics and responsibility issues related to care robots. In the second phase, ideas from different stakeholders regarding the viewpoints are studied, and those ideas are compared to the ones presented in previous publications. The aim of this study is to find solutions to the issues presented in scientific literature and, also, to find new issues for consideration and further studies. The study is qualitative, and a theme interview was utilized as the main method for acquiring knowledge. The study is a part of the SHAPES Horizon 2020 project. From the perspective of SHAPES, the aim of the study is to provide useful knowledge for the project, which would in part promote the goal of SHAPES, i.e., the development of an international healthcare ecosystem. Based on the results of the study, it can be argued that the issues presented in previous academic publications regarding the ethics and accountability of robots in practical healthcare work are not relevant. Both the legislation and the logic of the AI algorithms used by care robots prevent those situations presented in previous academic discussions in which robots would presumably be forced to make decisions demanding ethical consideration. The results also point toward the fact that current legislation does not limit the development of healthcare robots more than it limits healthcare work in general. Thus, the considerations of ethics regarding care robots should rather be focused on the threshold values used by robots, when making interpretations, as well as the data used for the purpose of machine learning. These were identified as potential subjects for further research.