AI4PublicPolicy / News (Page 5)

The Data Management component is in charge of the collection and management of the datasets from different stakeholders in the AI4PublicPolicy. The dataset management component loads and stores the data to be analyzed by the Virtualized Policy Management Environment (VPME). Data can be collected from...

In the second and final part of the blog series on the Infrastructure of AI4PublicPolicy services, light is shed on the Compute and Data Federation layer and the Platforms layer.   Compute and Data Federation The Compute and Data Federation layer provides advanced solutions to manage the...

The AI4PublicPolicy services are built on top of the multi-layered EOSC Compute Platform Its main layers are the Federated Resource Providers layer, the Compute and Data Federation layer, the Platforms layer, and the vertical Service Management Tools layer. The vertical Service Management Tools layer includes Helpdesk, Monitoring,...

In our previous blog post about the main components of the AI4PublicPolicy platform, the AI Security, AutoML and Text and Sentiment Analysis components have been described – find the 4th blog post on the platform components here. In this 5th and final part of the AI4PublicPolicy...

In our previous blog post about the main components of the AI4PublicPolicy platform, the XAI (eXplainable AI) and the Policy Explainability and Interpretation components have been described – find the 3rd blog post on the platform components here. In this fourth part of the AI4PublicPolicy platform...

In our previous blog post about the main components of the AI4PublicPolicy platform, the Cross Country Interoperability and Datasets and Policies Catalogue components have been described – find the 2nd blog post on the platform components here. In this third part of the AI4PublicPolicy platform components’...

In our previous blog post about the main components of the AI4PublicPolicy platform, the Policy and Datasets Management and the Semantic Interoperability components have been described – find the 1st blog post on the platform components here. In this second part of the AI4PublicPolicy platform components’...

The main components of AI4PublicPolicy platform are logically grouped into three main modules: Reusable and Interoperable Policies, Transparent and Trusted AI for Policy Management and AI Tools for Autonomous Policy Making. All these components are independent components that provide REST interfaces. The architecture notation is...

The AI4PublicPolicy Policy Making Process leverages on the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to provide a data-driven, AI and evidence-based approach for policy development, while fostering the collaboration between policy makers and AI experts. Through CRISP-DM the project, also, aims to involve...