Ethical Decision-Making for Social Robots in Elderly Care Scenario: A Computational Approach

As integrating social robots in elderly care scenarios becomes increasingly prevalent, the need for ethical decision-making frameworks to govern their actions is critically important. This paper presents a comprehensive computational approach using supervised machine learning algorithms to address the ethical considerations inherent in robot-assisted fetching tasks for the elderly. Drawing upon established ethical principles and novel moral dimensions specific to elderly care, we develop an intricate framework encompassing diverse entities and scenarios using a greet or beat approach. To validate the framework, we conducted a pilot study involving thirty participants experienced in caregiving. Through an interactive application, participants designed scenarios, decided whether the robot should fetch objects, and provided reasons for their choices. Their decisions were then compared with predictions generated by a set of machine learning algorithms trained on a dataset of various scenarios. Our results shed light on the diverse ethical perspectives in elderly care and the feasibility of automating ethical decision-making for social robots in this domain. This research contributes to the burgeoning field of roboethics, offering insights and tools to guide the responsible deployment of robots in assistive elderly care, ultimately promoting the well-being and ethical treatment of elderly individuals.