Explainable Hybrid AI 

Blend rules with machine learning and keep every decision traceable.

Introduction

The ULTIMATE Horizon Europe project (Grant 101070162) is developing hybrid AI that combines physics based rules with data driven models. This mix raises predictive accuracy beyond rules alone while preserving the step by step transparency that black box methods lack. The architecture includes explanation, validation and ethics checks for use cases ranging from satellite fault detection to human robot collaboration.

AI ORA’s contribution

We provided the ethical risk framework and trust checklist that every hybrid algorithm must satisfy before release. Checkpoints match draft EU AI Act duties and common safety engineering gates, giving engineers and auditors a clear record of each rule and machine learning interaction. This work shows our deep experience in uniting symbolic and statistical AI without losing traceability.

Project impact

Adopting Explainable Hybrid AI brings three clear gains

  • Ready for safety critical roles. Organisations keep control of logic while enjoying the power of machine learning.
  • Faster diagnosis. Engineers can trace both rule paths and model inputs for any output, which cuts fault finding time.
  • Stronger compliance. Built in explanations meet growing customer and regulatory requests for transparency.

The trust checklist shapes our Readiness Accelerator reviews and supports Solution Development sprints, so every project benefits from tested explainability patterns.

Why it matters to business leaders

Deploy advanced AI, meet safety requirements and keep engineers in command, all inside your current quality framework.

Learn more

Explore ULTIMATE project deliverables here: https://ultimate-project.eu/

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