We propose a conceptual, empirically testable framework for Robot Fostering, -a curriculum-driven, governance-first approach to domestic robot deployments, emphasizing long-term, curated interaction trajectories. We formalize trajectory quality with quantifiable metrics and evaluation protocols aligned with EU-grade governance standards, delineating a low-resource empirical roadmap to enable rigorous validation through future pilot studies.

As artificial intelligence (AI) reshapes industries and societies, ensuring its trustworthiness -- through mitigating ethical risks like bias, opacity, and accountability deficits -- remains a global challenge. International Organization for Standardization (ISO) AI standards, such as ISO/IEC 24027 and 24368, aim to foster responsible development by embedding fairness, transparency, and risk management into AI systems. However, their effectiveness varies across diverse regulatory landscapes, from the EU' s risk - based AI Act to China's stability - focused measures and the U.S.'s fragmented state - led initiatives. This paper introduces a novel Comparative Risk - Impact Assessment Framework to evaluate how well ISO standards address ethical risks within these cont exts, proposing enhancements to strengthen their global applicability. By mapping ISO standards to the EU AI Act and surveying regulatory frameworks in ten regions -- including the UK, Canada, India, Japan, Singapore, South Korea, and Brazil -- we establish a ba seline for ethical alignment. The framework, applied to case studies in the EU, US - Colorado, and China, reveals gaps: voluntary ISO standards falter in enforcement (e.g., Colorado) and undervalue region - specific risks like privacy (China). We recommend man datory risk audits, region - specific annexes, and a privacy - focused module to enhance ISO's adaptability. This approach not only synthesizes global trends but also offers a replicable tool for aligning standardization with ethical imperatives, fostering int eroperability and trust in AI worldwide. Policymakers and standards bodies can leverage these insights to evolve AI governance, ensuring it meets diverse societal needs as the technology advances .

As the uses of artificial intelligence (AI) continue to expand, there is a growing need for effective risk management to deal with issues ranging from technical, such as algorithm failures, to ethical, including bias in decision-making. A new ISO/IEC Standard provides essential guidance on risk management for organizations of all sizes and types that utilize AI in their systems or processes. ISO/IEC 23894 shows users how to manage AI-related risks effectively in order to achieve objectives and improve performance. "While AI systems are similar to traditional IT systems in many ways, they also present new aspects such as their ability to learn," says Wael William Diab, who chairs the joint IEC and ISO committee that develops AI standards. "SC 42 took the novel approach of developing a framework that employs well-established techniques around risk management. ISO/IEC 23894 provides a holistic and proactive approach to managing AI-related risks with the goal of enabling users to manage the risks effectively to harness the full potential of AI."