Policy Cloud has presented a paper on the main research challenges and solutions to and for data-driven policy management at this year's AIAI event (Artificial Intelligence Applications and Innovations). The conference provided an opportunity for Policy Cloud, an EU project led by ATOS and a number of EU Partners and funded under the European Commission’s H2021 programme, to present its innovative approach and solutions to policymaking. The project aims to harness the potential of digitisation, big data and cloud technologies to improve the modelling, creation and implementation of policy. In three years (2020-2023) the project will address challenges faced by many businesses and public administrations of improving how they make policy decisions by accessing and using data. Currently, four Policy Cloud pilot cases are focusing on specific and relevant policy challenges: Radicalicalisation, Food Value Chain, Urban Environment and Policies for Citizens.
The academic paper, which has been published in IFIP Advances in Information and Communication Technology, outlines Policy Cloud’s goal of supporting public authority decision making for policy modelling, implementation and simulation, as well as for policy enforcement and adaptation. It outlines several challenges and solutions on the best way to optimise policies across public sectors exploiting enormous quantities of data and the interoperability of diverse data sets. The seven research challenges outlined in the article are mostly technical challenges but also include some of the societal obstacles.
This book constitutes the refereed proceedings of the 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, held virtually and in Hersonissos, Crete, Greece, in June 2021.
The 50 full papers and 11 short papers presented were carefully reviewed and selected from 113 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: adaptive modeling/ neuroscience; AI in biomedical applications; AI impacts/ big data; automated machine learning; autonomous agents; clustering; convolutional NN; data mining/ word counts; deep learning; fuzzy modeling; hyperdimensional computing; Internet of Things/ Internet of energy; machine learning; multi-agent systems; natural language; recommendation systems; sentiment analysis; and smart blockchain applications/ cybersecurity.
Chapter “Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-based Approaches: Challenges and Perspective” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
One of the key elements in several application domains, such as policy making, addresses the scope of achieving and dealing with the very different formats, models and languages of data. The amount of data to be processed and analyzed in modern governments, organizations and businesses is staggering, thus Big Data analysis is the mean that helps organizations to harness their data and to identify new opportunities. Big Data is characterized by divergent data coming from various and heterogeneous sources and in different types, formats, and timeframes. Data interoperability addresses the ability of modern systems and mechanisms that create, exchange and consume data to have clear, shared expectations for the context, information and value of these divergent data. To this end, interoperability appears as the mean for accomplishing the interlinking of information, systems, applications and ways of working with the wealth of data. To address this challenge, in this paper a generalized and novel Enhanced Semantic Interoperability approach is proposed, the SemAI. This approach primarily focuses on the phases of the translation, the processing, the annotation, the mapping, as well as the transformation of the collected data, which have a major impact on the successful aggregation, analysis, and exploitation of data across the whole policy making lifecycle. The presented prototype and its required subcomponents associated with this approach provide an example of the proposed hybrid and holistic mechanism, verifying its possible extensive application and adoption in various policy making scenarios.
To read more from the AIAI2021 paper you can find it here: https://www.springerprofessional.de/en/semai-a-novel-approach-for-achieving-enhanced-semantic-interoper/19280440