It is needless to mention the (already established) overarching importance of knowledge organization and its tried-and-tested high-quality schemes in knowledge-based Artificial Intelligence (AI) systems. But equally, it is also hard to ignore that, increasingly, standalone KOSs are becoming functionally ineffective components for such systems, given their inability to capture the continuous facetization and drift of domains. The paper proposes a radical re-conceptualization of KOSs as a first step to solve such an inability, and, accordingly, contributes in the form of the following dimensions: (i) an explicit characterization of Knowledge Organization Ecosystems (KOEs) (possibly for the first time) and their positioning as pivotal components in realizing sustainable knowledge-based AI solutions, (ii) as a consequence of such a novel characterization, a first examination and characterization of KOEs as Socio-Technical Systems (STSs), thus opening up an entirely new stream of research in knowledge-based AI, and (iii) motivating KOEs not to be mere STSs but STSs which are grounded in Ethics and Responsible Artificial Intelligence cardinals from their very genesis. The paper grounds the above contributions in relevant research literature in a distributed fashion throughout the paper, and finally concludes by outlining the future research possibilities.

While AI can lift competition and productivity, it also can act as a great leveler, putting smaller players on the same footing as goliaths. Take pharmaceutical research, for example. Large companies have the budget and resources to physically test millions of drug candidates, giving them an advantage over startups and researchers. But smaller labs can achieve similar results by harnessing neural networks that simulate how a potential drug molecule will bind with a target protein. Deep learning can help smaller companies and other researchers discover promising drug treatments by improving the speed and accuracy of molecular docking, the process of computationally predicting how and how well a molecule binds with a protein.