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

TL;DR

This paper introduces a collaborative human-AI framework for building inspectable semantic structures that enhance ethical decision-making in AI by grounding actions in explicit, justifiable evidence accessible to all.

Contribution

It presents a novel collaborative process for creating transparent semantic layers in AI, integrating expert validation to improve decision justification and mitigate institutional memory loss.

Findings

Improved response quality and efficiency

Enhanced transparency and inspectability

Mitigation of institutional amnesia

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.