The RAIDO project participated in European Data Week 2026, held on 5-6 May 2026, contributing to the expert session “When Data Pipelines Decide AI Trust and Efficiency: Theory and Practice”. The session brought together five EU-funded projects to exchange insights and approaches for building trustworthy, efficient, and data-driven Artificial Intelligence systems.

Organised in collaboration with PANDORA, AI-DAPT, MANOLO, EXTRA-BRAIN and TURING under the European Commission’s “Efficient Trustworthy AI — Making the Best of Data” portfolio, the workshop explored one of the key challenges currently facing the AI ecosystem: transforming fragmented, low-quality, or underutilised datasets into reliable and trustworthy AI-ready resources.

Representing the RAIDO consortium, Christos Vasilakis of Metamind Innovations (MINDS) presented the project’s approach to trustworthy AI, highlighting how RAIDO combines advanced data management, Green AI techniques, explainability mechanisms, and accountability frameworks to support the development of efficient and transparent AI systems across cloud-edge environments.

RAIDO’s Four-Pillar Approach to Trustworthy AI

RAIDO’s vision for trustworthy AI is built around four complementary pillars that collectively address the quality, efficiency, transparency, and accountability of AI systems.

Pillar I — Data Quality & Fairness

RAIDO promotes trustworthy AI by ensuring that AI models are trained on high-quality and unbiased datasets. Through automated data curation, synthetic data generation using Digital Twins and diffusion models, and FAIR-compliant federated data mining, the project supports the creation of reliable AI-ready data while preserving data sovereignty at the edge.

Pillar I — Efficiency & Sustainability

The project integrates advanced Green AI techniques, including knowledge distillation and few-/zero-shot learning, alongside a self-evolving orchestration framework designed to optimise both model performance and computational efficiency. RAIDO targets measurable reductions in training time, energy consumption, and carbon emissions, contributing to more sustainable AI deployment practices.

Pillar III — Transparency & Explainability

To improve trust and interpretability, RAIDO incorporates explainability and trust assessment mechanisms throughout the AI lifecycle. Combining approaches such as SHAP and LIME explanations, bias detection, and human-in-the-loop feedback, the framework enables traceable and auditable AI decision-making processes.

Pillar IV — Accountability & Traceability

RAIDO further strengthens AI accountability through blockchain-secured audit logs, NFT-based model certification, and IPFS-enabled storage mechanisms. These technologies support immutable and GDPR-compliant tracking of training, deployment, and inference activities across distributed cloud-edge infrastructures.

Smart Energy Pilot in Focus

The presentation also highlighted RAIDO’s smart-grid pilot in Seville, Spain, where AYESA is deploying the platform to predict photovoltaic panel power generation through a fully local execution pipeline. The pilot integrates automated data processing, AI inference, and human-centred decision-making mechanisms, aiming to support reduced CO₂ emissions, improved energy efficiency, and more effective operational planning.

Looking Ahead

As RAIDO enters its final year, the project continues advancing trustworthy and sustainable AI technologies through collaboration among approximately 29 partners across 12 countries and the development of four real-world pilot environments. The participation in European Data Week 2026 further reinforced RAIDO’s contribution to the European discussion on efficient, transparent, and trustworthy AI, supporting the transition from cutting-edge AI research to practical and socially responsible deployment.

About RAIDO

RAIDO is a Horizon Europe-funded research and innovation project focused on advancing Trustworthy and Green AI through an integrated framework covering data management, model optimisation, explainability, and sustainable AI deployment. The project develops innovative methods for automated data curation and enrichment, including Digital Twins and diffusion models, alongside data- and compute-efficient AI techniques such as few-/zero-shot learning, continual learning, and model distillation. RAIDO further integrates explainability, blockchain-enabled traceability, reinforcement learning, and AI orchestration mechanisms to support transparent, accountable, and energy-efficient AI systems across cloud-edge environments. The platform is being validated through four real-world demonstrators in application domains including smart grids, smart farming, healthcare, and robotics.

RAIDO project has received funding under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101135800. UK participants in Horizon Europe Project Raido are supported by UKRI.

Download the press release here: RAIDO Showcases Trustworthy AI in Action at European Data Week 2026

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