From Complex Genomic Data to Clear Guidance: The RAIDO Pharmacogenomics Pilot
Medicines do not affect everyone in the same way. Genetic differences can influence whether a drug works as expected, whether a different dose may be needed, or whether a person is more likely to experience adverse effects. Pharmacogenomics, commonly known as PGx, uses this information to support safer and more personalised medication-related decisions. However, applying […]
Smarter AI for Sustainable Farming: How RAIDO is Making Agricultural AI More Efficient
Artificial Intelligence has the potential to transform agriculture and food production. From monitoring crop health to optimizing fungal cultivation for alternative protein production, AI can help producers make faster, more informed decisions while reducing waste and improving sustainability. However, building reliable AI systems for agriculture comes with significant challenges. High-performing AI models typically require vast […]
Advancing Climate Justice through Green AI: Insights from the Climate Justice Forum
The Awareness Movement (AWM), a partner of the RAIDO Project, works to promote sustainable development and strengthen the connection between technological innovation, environmental sustainability, and civil society. Within RAIDO, AWM contributes to connecting the project’s digital innovations with civil society, grassroots communities, and real-world environmental objectives. Recently, AWM Vice President, Mrs. Lanika Angelidou, represented the […]
Building Ethical AI Before Deployment: The RAIDO Ethics Helpdesk
When we talk about trustworthy artificial intelligence (AI), conversations often focus on making AI systems more transparent or explainable. But before we can explain how an AI system reaches its decisions, we first need to ensure it has been built on a strong ethical foundation. This is the principle behind ethics-by-design (Nurock et al., 2021): […]
Measuring What Matters: How Bias Detection in Training Data Supports EU AI Act Conformity
As the EU AI Act moves from legislation to enforcement, one of its most operationally demanding requirements sits in Article 10 data and data governance. For high-risk AI systems, it is no longer enough to collect data and train a model. Providers must demonstrate that their datasets were examined earlier for bias, that sensitive demographic […]
RAIDO Piloting and Validation
Piloting and Validation activities are essential components of any technical project as a means to systematically verify that the designed solution meets functional, performance, and user requirements under realistic conditions. RAIDO dedicates Work Package 6 to the integration, demonstration and assessment of the project’s core platform. Netcompany SA, as a leading European IT solutions and […]
The RAIDO Data Lake: Enabling reliable and sustainable Artificial Intelligence
Modern Artificial Intelligence relies on data. As AI solutions become more sophisticated and are applied across different domains, managing datasets efficiently, securely, and sustainably becomes significantly important. This is exactly the challenge addressed by the RAIDO Data Lake, which serves as the central information management layer of the platform. The RAIDO Data Lake brings together […]
RAIDO Green AI Orchestration in TinyML: Towards Enabling Sustainable Innovation for Developing Countries
Artificial intelligence is often associated with massive data centers and energy-intensive computation. However, a new generation of lightweight and energy-efficient AI is emerging through TinyML — the deployment of machine learning models on ultra-low-power microcontrollers and embedded devices. Combined with AI orchestration, TinyML has the potential to democratize access to intelligent systems while supporting global […]
Building Smarter Data Pipelines: How RAIDO Is Transforming AI-Ready Data Preparation
How the RAIDO project is automating data curation, annotation and federated mining to power trustworthy AI High-quality training data is the backbone of any reliable AI system. However, data preparation turns out to be the most challenging phase of the machine learning lifecycle in terms of required efforts and the likelihood of mistakes. According to […]