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 Big Data Value - BDV was defined by the Big Data Value Association - BDVA and is a framework to locate AI4PublicPolicy data-related technologies. The reference model is structured into different horizontal and vertical concerns as depicted in graphical representation below. The horizontal concerns...
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...
Machine learning has the potential to improve the way public policy is made. Policy makers are interested in new methods of understanding and analyzing data that will help them make better decisions. There is a layer of human ingenuity missing from existing models. True machine...
Co-creating digital tools for better govenance Five H2020 e-governance projects have joined efforts in the Data Driven Policy Cluster to promote the use of European cloud infrastructures for public administrations and policy making. The Challenge Digital technologies have changed the world. Today people expect faster, seamless, on-demand services...
AI4PublicPolicy is working on the interoperability of the Virtualized Policy Management Environment (VPME) of the project with EOSC, with the main interest of facilitating access to the Cloud and HPC resources to enable the project’s AI tools. At the same time it is expected to...
#novoville #ai4publicpolicy #technologies #AI #Incidentmanagement #Chatbot #smartparking #H2020 #EUfunded Novoville allows citizens to report issues they encounter in their neighborhood (e.g., potholes, broken traffic signs, fly tipping) straight from their mobile devices, and receive automated notifications about the progress and resolution of their issue. All citizen requests received either...