Meet GRACE: a moral governor for safer, more transparent AI

Kari Jaaskelainen

16 Jan 2026 — 1 min read

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing?

Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making.

Moral Module: uses deontic logic and explicit reasons to decide which high-level actions are permissible.
Decision-Making Module: the wrapped AI plans optimal low-level actions, but only within the moral boundaries.
Guard: monitors and enforces compliance, enabling formal checks and statistical guarantees.

Because GRACE reasons with explicit, symbolic factors, its decisions are interpretable, contestable, and justifiable—so stakeholders can inspect, debate, and refine what counts as acceptable behavior.

The authors demo GRACE on a therapy assistant built on an LLM, showing how the system prevents harmful suggestions while still being helpful.

Paper: https://arxiv.org/abs/2601.10520v1

Paper: https://arxiv.org/abs/2601.10520v1

Register: https://www.AiFeta.com

AI AIAlignment AIEthics Safety NeuroSymbolic DeonticLogic LLM ResponsibleAI

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added

AI that stays on track for days: ML-Master 2.0

AI is great at quick tasks—but stumbles on week-long projects. This paper tackles that ultra-long-horizon gap. Meet ML-Master 2.0, an autonomous agent for machine learning engineering that stays strategically coherent over days. Its core idea, Hierarchical Cognitive Caching (HCC), treats memory like a multi-level cache and a lab

Read more

Why some fine-tuned LLMs miss phishing—and how to fix it

AI that explains itself—by following the science

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

AI that stays on track for days: ML-Master 2.0