Artificial data is possible – but comes with the risk of inbuilt bias..
Articles on Synthetic data
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AI models can simulate the answers thousands of people would provide to a survey, but the results aren’t a reliable measure of what real people would actually say.
Urban digital twins pull together tremendous amounts of data to realistically model cities, but they’ve been missing a critical ingredient: people.
To overcome two challenges in training AI – scarce or hard-to-get data and data privacy – researchers have come up with a counterintuitive technique: fake it.
Artificial intelligence is so hungry for information, soon there will be no real data left. If done right, synthetic data can fill the gap and improve AI models.
‘Synthetic’ data is becoming common in policing, hospitals and government.
Generative AI advances bring risks of model collapse. Here’s how synthetic data can address these risks.
Top contributors
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Professor of Statistics and Professor of EECS, University of Michigan
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Professor of gender and society, Linköping University
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Lecturer, Liberal Arts and Sciences, OCAD University
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Senior Lecturer in Computer Science, RMIT University Vietnam
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Associate Professor of Public Affairs and Planning, University of Texas at Arlington
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Assistant Professor, Department of Law, Bocconi University