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

arXiv: https://arxiv.org/abs/2512.04822 · HTML: https://mlsanity.com/paper/2512.04822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 4 Dec 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Built on by (most cited first, 1 of 1)

- [From Traceability to Justifiability: Accountability Structures in Agentic Software Engineering](https://mlsanity.com/paper/2608.23610/) (2026)

## Abstract (arXiv)

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.