Ethical Responsibility and AI: A Healthcare Robotics Case Study

Thesis Abstract

This study investigates ethical responsibility in the deployment of AI-enabled healthcare robotics within hospital settings, addressing the normative and practical tensions that arise when autonomous systems mediate patient care, data privacy, informed consent, and accountability. The central aim is to illuminate how organizational governance, professional ethics, and AI design interact to shape responsible outcomes, and to propose a framework for integrating ethical considerations into routine clinical workflows. Specific objectives include (1) identifying salient ethical challenges faced by clinicians, patients, and administrators when interacting with robotic systems, (2) examining how institutional policies, regulatory norms, and professional codes influence responsibility allocation among humans and machines, (3) evaluating the operational impact of transparency, explainability, and data governance on trust and acceptance, and (4) developing a normative and practical model for ethical responsibility that can guide governance, risk management, and system design in healthcare robotics. A mixed-methods design combines qualitative and quantitative approaches to triangulate findings. The study is conducted in three tertiary care hospitals within a metropolitan region, with a total over-time population of clinical staff, biomedical engineers, and management personnel. Purposive sampling yields 60 senior clinicians, 20 nurses, 15 roboticists, and 10 hospital administrators for qualitative interviews, ensuring representation across departments where AI-enabled robots are deployed. In addition, a stratified random sample of 250 patients who encountered AI-assisted procedures or interactions with robotic systems is surveyed to assess perceptions of responsibility, trust, and consent. Data collection uses semi-structured interviews, focus groups with clinicians and engineers, document analysis of institutional policies, and standardized survey instruments incorporating the Moral Responsibility Scale (MRS) and Trust in Automation Index (TAI). Instrument validity is enhanced through expert panels and pilot testing with 15 participants, and reliability is assessed via Cronbach’s alpha for multi-item scales. Analytical approaches include thematic analysis for interview and focus group transcripts, guided by grounded theory coding to uncover emergent ethical themes. Quantitative data are analyzed using multiple regression to test hypotheses about predictors of perceived responsibility, including system transparency, explainability, severity of risk, and institutional governance. Structural equation modeling (SEM) is employed to test a conceptual model linking ethical responsibility to trust, acceptance, and clinical efficacy. The study also applies a deontological and virtue ethics lens, drawing on the theories of Kantian duty and virtue ethics as interpretive frameworks, complemented by the principlism model (autonomy, beneficence, non-maleficence, justice) to interpret findings and to triangulate normative claims with empirical data. A policy-ethics synthesis assesses how existing regulatory instruments—such as informed consent standards, data protection regulations, and accountability guidelines—map onto practical responsibilities in robotic-assisted care. Expected findings indicate that perceived responsibility is distributed across clinicians, hospital governance, and designers of AI systems, with higher perceived responsibility associated with greater system transparency and explicit accountability roles. The analysis is anticipated to reveal that opaque decision-making in robotic agents correlates with reduced patient trust and calls for clearer delineation of liability in mixed human–machine decision chains. The study is expected to identify a set of operational indicators—risk communication protocols, audit trails, and decision-logging practices—that strengthen ethical accountability without compromising clinical efficiency. The contribution to knowledge lies in (i) advancing an integrative, theory-informed framework that operationalizes ethical responsibility in healthcare robotics, (ii) providing empirical evidence on how governance, design, and clinical work practices converge to shape moral accountability, and (iii) offering a set of actionable recommendations for policymakers, hospital bodies, and AI developers to enhance transparency, consent processes, and liability clarity in AI-enabled healthcare. The concluding section will articulate practical guidelines for implementing a responsibility governance model, including explicit roles, audit mechanisms, and design requirements for explainable AI in hospital robotics, as well as pathways for future research on cross-border regulatory harmonization and long-term patient outcomes.