Humanoid Robots, Public Trust, and Ethical Algorithm Design: A Multidisciplinary Framework for Societal Integration and Future Adoption

Abstract

This study investigates the complex relationship between public trust and the ethical design of behavioral algorithms in humanoid robots, drawing on a qualitative, interdisciplinary narrative review. By synthesizing research across AI ethics, Human–Robot Interaction (HRI), behavioral science, and cultural studies, it demonstrates that public acceptance depends not only on technical performance, but also on emotional, symbolic, and cultural factors that shape trust and legitimacy. Key findings reveal that ethical principles such as transparency and fairness are interpreted through local social contexts, and current algorithmic frameworks often fail to address the full diversity of user expectations. As an original contribution, the study introduces the Human–Robot Integration Ethics Matrix (HR-IEM), a conceptual tool linking core algorithmic objectives to measurable social trust indicators. The HR-IEM bridges the persistent gap between abstract ethical standards and lived human experience, offering actionable guidance for developers and policymakers to evaluate ethical–social alignment in real-world settings. The analysis also identifies critical methodological limitations, including an overreliance on laboratory studies and a lack of participatory and cross-cultural design practices. To address these gaps, the study advocates for adaptive ethics modules, participatory feedback mechanisms, and context-sensitive frameworks that support the co-production of social legitimacy. Ultimately, this research presents a new interdisciplinary agenda for the responsible integration of humanoid robots, emphasizing cross-sector collaboration and the continuous calibration of ethical design to evolving public values.