Bridging Human-Machine Knowledge for Responsible AI

The rapid evolution of artificial intelligence (AI) accentuates the need for responsible systems that integrate ethical considerations with robust safety and sustainable measures. This chapter explores how principles from knowledge management (KM) can facilitate better translation and sharing of expertise between humans and machines, thereby enabling more accountable and transparent AI systems. By synthesising insights from applied ethics and human-machine communication frameworks, I argue that a KM-centric approach can bridge semantic gaps and foster interdisciplinary collaborations that enhance both AI safety and ethical practices. Central to the discussion is the notion that responsible AI development must encompass not only technical safety measures but also mechanisms of knowledge curation, translation, and dissemination. The integration of explainable AI tools and effective human-machine interfaces with development of systems that can demystify AI decision-making processes and promote trust among users. This dual emphasis on ethical guidelines and safety protocols ensures that AI systems remain comprehensible, auditable, and adaptive to evolving economic and social norms. Furthermore, drawing on established frameworks in AI ethics and applied ethics, the chapter highlights that knowledge-sharing practices range from formal codes to collaborative research and can serve as critical conduits for mitigating risks and aligning AI behaviours with human values. Through case studies and empirical illustrations drawn from interdisciplinary research, the discussions demonstrate how effective communication between human and machine agents can lead to improved decision-making and safety thresholds. Such practices not only enhance system reliability but also empower users, ensuring that ethical imperatives are at the forefront of AI design and deployment. In conclusion, this chapter advocates for a paradigm shift where KM is recognised as a foundational element not just between humans but human-to-machine in crafting responsible AI that is both safe and ethically sound, a consideration that takes a humanistic position.