Project description
Synthetic data and its societal ethics
AI-generated datasets used to train algorithms are changing the ethical landscape of modern society. The ERC-funded SYNDATA project explores how these datasets influence representation, privacy, and fairness in sectors such as healthcare, finance, and government. By generating data where real-world collection is impossible or ethically sensitive, AI promises to fill gaps in diversity and protect confidentiality, but also introduces new risks. SYNDATA pioneers the first large-scale social science study of synthetic data, combining historical analysis with investigations of current applications, from biometrics to weapons detection. The project’s goal is to redefine how society understands the ethical consequences of algorithmic data generation.
Objective
SYNDATA develops a novel approach to understanding the ethical consequences of synthetic data for contemporary societies. Machine learning algorithms and AI models such as Gemini and GPT-4 are increasingly used to generate data (called ‘synthetic data’), which is in turn used to train other algorithmic models. This is leading to transformations in social and political life, because synthetic data embody a promise to address an array of ethical challenges associated with AI, such as the lack of ethnic diversity and gender representation in large training datasets, in addition to issues of privacy and confidentiality in sensitive datasets. In short, if some data cannot be collected in the real world, it can be generated via algorithms. This is particularly relevant in societal domains – such as healthcare, finance, and government – where an ethical question in the use of sensitive personal data is common. While there is a substantive literature outlining the various effects of data and algorithms on society, the area of synthetic data remains both under-researched and under-theorised. As such, SYNDATA will pioneer the first large-scale, systematic social science study of synthetic data. By examining the three pressing issues of generativity, representation, and resistance, the project will take seriously the ways that synthetic data are generated whilst also transforming how contemporary algorithms and AI models are trained and deployed in society. In order to do this, the project will conduct both archival research into the historical antecedents of synthetic data as well as path-defining studies of different areas where synthetic data is currently being generated and deployed as a way to train algorithmic systems: biometrics and facial recognition as well as weapons ammunition detection. SYNDATA will provide cutting-edge social science knowledge on how the emergence of synthetic data is transforming the relationships between data, AI, and ethics in society.
Fields of science (EuroSciVoc)
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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.
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-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-STG
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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.
YO10 5DD YORK NORTH YORKSHIRE
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.