AI research and implementations are growing, and so are the risks associated with AI (Artificial Intelligence) developments, especially when it’s difficult to understand exactly what they do and how they work, both at a localized level, and at deployment, in particular when distributed and on a large scale. Governments are pouring massive funding to promote AI research and education, yet research results and claims, as well as the effectiveness of educational programmes, can be difficult to evaluate given the limited reproducibility of computations based on ML (machine learning) and poor explainability, which in turn limits the accountability of the systems and can cause cascading systemic problems and challenges including poor reproducibility, reliability, and overall lack of trustworthiness. This paper addresses some of the issues in Knowledge Representation for AI at system level, identifies a number of knowledge gaps and epistemological challenges as root causes of risks and challenges for AI, and proposes that neurosymbolic and hybrid KR approaches can serve as mechanisms to address some of the challenges. The paper concludes with a postulate and points to related and future research

The integration of responsible artificial intelligence (RAI) principles with emerging neurosymbolic AI (NSAI) systems is crucial for the development of fair, explainable, and trustworthy AI technologies. This paper presents a systematic review exploring the convergence of RAI and NSAI, analyzing current research to assess how RAI principles such as explainability, bias, robustness, transparency, and privacy have been applied to NSAI. This work employed a systematic literature review to synthesize findings from a sample of papers demonstrating RAI principle implementations. Our analysis reveals two main trends: significant research demonstrates the application of NSAI to enhance RAI principles in other AI systems, while limited work directly applies RAI principles to NSAI architectures. Key challenges include the lack of established frameworks for implementing RAI within NSAI systems and the com- plexities inherent in merging neural and symbolic reasoning methods. This review highlights open research gaps and suggests pathways for future work, emphasizing the need for robust RAI frameworks tailored to NSAI systems.