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Learning Network for AI and Data-Powered Social Innovation and Co-Creation of Transformative Change

Project description

Responsibly and ethically advancing AI and data science

The rapid advancement of AI and data science has driven the growth of new technologies, including the Internet of Things and digitalisation. However, despite their benefits, these technologies can be easily misused for unethical purposes or lead to serious issues. Supported by the Marie Skłodowska-Curie Actions programme, the Data2Action project recognises the importance of these technologies and risks of these technologies when mishandled. The project aims to train 10 doctoral fellows in social innovation, data science, and AI, enabling them to work ethically and responsibly on further developments and advancements. The project will also encourage social innovation, demonstrate its benefits, and build tools and support policies to expand its reach.

Objective

Data2Action will train 10 doctoral Fellows as the next generation of transdisciplinary social innovation leaders who are capable of ethically and responsibly developing state-of-the-art data science and artificial intelligence (AI) for social good. The network will bring together leading data science and AI researchers and experts with practitioners and experts in social innovation to conduct empirical research and cascading skill-building with the following objectives:

Objective 1: Establish a research roadmap and framework grounded in empirical knowledge and best practices for data science/AI to power social innovation (Data/AI for Social Good).

Objective 2: Demonstrate the potential and benefits across sectors of implementing social innovations powered by data science and AI through five demonstrator projects that showcase the impact on critical societal issues: Climate, Social justice, Democracy, and Health and Ageing.

Objective 3: Build capacity, Data/AI tools and methods for social innovators and entrepreneurs through an integrated open cascade training programme.

Objective 4: Create new career pathways for data science and AI dedicated to social innovation.

Objective 5: Provide guidance and support for practice about data science and AI for social innovation to stakeholders who contribute to social innovation: policymakers, funders, and governmental organisations and to data science and AI technology development.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2023-DN-01

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Coordinator

UNIVERSITEIT GENT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 525 240,00
Address
SINT PIETERSNIEUWSTRAAT 25
9000 GENT
Belgium

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Region
Vlaams Gewest Prov. Oost-Vlaanderen Arr. Gent
Activity type
Higher or Secondary Education Establishments
Links
Total cost

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No data

Participants (3)

Partners (30)

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