Trusted AI for Transparent Public Governance fostering Democratic Values
AI4Gov is a joint effort of policy makers, public institutions / organizations, legal, Social Science and Humanities and Big Data/AI experts to unveil the potentials of Artificial Intelligence (AI) and Big Data technologies for de...
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Información proyecto AI4Gov
Duración del proyecto: 35 meses
Fecha Inicio: 2023-01-01
Fecha Fin: 2025-12-31
Líder del proyecto
MAGGIOLI SPA
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
3M€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
AI4Gov is a joint effort of policy makers, public institutions / organizations, legal, Social Science and Humanities and Big Data/AI experts to unveil the potentials of Artificial Intelligence (AI) and Big Data technologies for developing evidence-based innovations, policies, and policy recommendations to harness the public sphere, political power, and economic power for democratic purposes. The project will also uphold fundamental rights and values standards of individuals when using AI and Big Data technologies. Hence, the project aims to contribute to the promising research landscape that seeks to address ethical, trust, discrimination, and bias issues by providing an in-depth analysis and solutions addressing the challenges that various stakeholders in modern democracies are faced with when attempts are made to mitigate the negative implications of Big Data and AI. In this direction, the project will introduce solutions and frameworks towards a two-fold sense, to facilitate policymakers on the development of automated, educated and evidence-based decisions and to increase the trust of citizens in the democratic processes and institutions. Moreover, the project will leverage the capabilities of state-of-the-art tools for providing un-bias, discrimination-free, fair, and trusted AI. These tools will be validated in terms of their ability to provide technical and/or organisational measures, causal models for bias and discrimination, and standardized methodologies for achieving fairness in AI.