Enabling Ethical AI: A case study in using Ontological Context for Justified Agentic AI Decisions
This addresses the need for ethical and transparent AI decisions in institutional settings, though it appears incremental by building on existing human-in-the-loop and explainable AI methods.
The paper tackles the problem of making AI agent decisions justifiable by introducing a collaborative human-AI approach to build an inspectable semantic layer, resulting in improved response quality and efficiency while capturing tacit institutional knowledge.
In this preprint, we present A collaborative human-AI approach to building an inspectable semantic layer for Agentic AI. AI agents first propose candidate knowledge structures from diverse data sources; domain experts then validate, correct, and extend these structures, with their feedback used to improve subsequent models. Authors show how this process captures tacit institutional knowledge, improves response quality and efficiency, and mitigates institutional amnesia. We argue for a shift from post-hoc explanation to justifiable Agentic AI, where decisions are grounded in explicit, inspectable evidence and reasoning accessible to both experts and non-specialists.