Information Ecosystem Reengineering (IER) -- the technological reconditioning of information sources, services, and systems within a complex information ecosystem -- is a foundational challenge in the digital transformation of public sector services and smart governance platforms. From a semantic knowledge management perspective, IER becomes especially entangled due to the potentially infinite number of possibilities in its conceptualization, namely, as a result of manifoldness in the multi-level mix of perception, language and conceptual interlinkage implicit in all agents involved in such an effort. This paper proposes a novel approach -- Representation Disentanglement -- to disentangle these multiple layers of knowledge representation complexity hindering effective reengineering decision making. The approach is based on the theoretically grounded and implementationally robust ontology-driven conceptual modeling paradigm which has been widely adopted in systems analysis and (re)engineering. We argue that such a framework is essential to achieve explainability, traceability and semantic transparency in public sector knowledge representation and to support auditable decision workflows in governance ecosystems increasingly driven by Artificial Intelligence (AI) and data-centric architectures.

I had the honour in early December to participate in an Interzone/Politik Video production with AI leaders: Danny Lange, Chief Data Scientist (CDS), Unity 3D, former CDS of Amazon, and Uber, Beena Ammanath, Executive Director of Deloitte's AI Institute Artificial Intelligence, hosted by John Koetsier where we explored a number of questions about how AI is leading to the fundamental reengineering of all aspects of our human experience, how we as a species interact with AI, and the implications of advancing our world without serious reflection on the type of world we want to build for future civilizations. The conversation we had allowed us to share our views, experiences, concerns and hopes for the New Year for a global society to think more deeply about the future world we want to create - together where AI is used for more good than harm. We have come together to fight Covid-19 and AI was a key enabler to bring to market vaccines, in unprecedented clinical trial R&D timeframes, to eradicate this virus, and help us get back to a more interactive global community where we can freely travel, visit our favourite restaurants and shop with more access in our local retailer stores. This is an excellent example of AI being used for good. However, much of AI in large global data sets are full of inequalities, incumbencies and biases of the innovators designing AI which have a direct impact on how the technology guides human information, perception and action.