Enabling Ethical AI: A case study in using Ontological Context for Justified Agentic AI Decisions

• URL: http://arxiv.org/abs/2512.04822v1
• Date: Thu, 04 Dec 2025 14:06:35 GMT
• Title: Enabling Ethical AI: A case study in using Ontological Context for Justified Agentic AI Decisions
• Authors: Liam McGee, James Harvey, Lucy Cull, Andreas Vermeulen, Bart-Floris Visscher, Malvika Sharan,
• Abstract summary: Authors show how this process captures institutional knowledge, improves response quality and efficiency, and mitigates institutional amnesia.<n>We argue for a shift from post-hoc explanation to justifiable Agentic AI, where decisions are grounded in explicit, inspectable evidence and reasoning to both experts and non-specialists.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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