The Ethics of Synthetic Data in the Age of Machine Learning and AI

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.