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In our previous blog post, the methodology and the process applied by the project pilots to define the datasets were outlined. In this blog post, we present a UML (Unified Modeling Language) model constructed from the data collected from the pilots' provided data sources. To begin,...

In order for the AI4PublicPolicy pilots to build policy models, it has been necessary to collect and analyse specific datasets from their available data sources. In this blog, we outline the methodology and the process applied by the project pilots to define such datasets. The data...

Part 2 – Architecture and Procedures In the first part of our blog series on the Policy Extraction and Recommendation toolkit developed for the AI4PublicPolicy platform, we discussed the design goals. In this second part, we delve into the architecture of the module. After analyzing state-of-the-art solutions,...

Part 1 – Solution Design The AI4PublicPolicy Policy Extraction and Recommendation module provides the tools required for the creation of an AI pipeline. The trained and tested AI models delivered through this pipeline allow the policymakers to design an appropriate policy. The design of the module has...

The Strategy Defence Library is one of the main components of the AI Security Toolbox inside the AI4PublicPolicy platform. It hosts state-of-the-art solutions against the most common and effective adversarial attacks. The Strategy Defence Library includes the best algorithms used for AI Adversarial Defence; and...

In the first part of our blog on Adversarial AI Defences, we introduced their generic categories. To tailor our suggestions to each AI4PublicPolicy use case and model, we've created the following table outlining the most promising and effective defences. This table includes the names of specific...

In our previous blog, in the context of securing machine learning, we conducted an in-depth analysis of Adversarial AI & Attacks. In this two-part blog series, our attention turns to the examination of Adversarial Defences. While early work in machine learning has often assumed a closed...

In recent years, the surge in Machine Learning (ML) performance, fueled primarily by Deep Learning (DL), has elevated its practical significance across various domains, including speech, image, and text processing. As ML techniques find applications in critical settings, such as the use of Convolutional Neural...

In the first part of our blogs on the Catalogue for Policies & Datasets component, we described its functionality and its data representation. The data described in the previous blog is stored in the AI4PublicPolicy Dataset Catalogue and could be accessed by a user through a...