Synthetic data as meaningful data: On responsibility in data ecosystems

摘要(出版方所载原文摘要) Synthetic data – algorithmically generated data – has been considered a novel solution to the data scarcity issue, and a ‘technical fix’ able to fill the gap in areas where real data is sensitive or biased. Different narratives about the nature of synthetic data as either mirroring or replacing real data, alongside diverse evaluation metrics for measuring the fidelity and utility of such data, have proliferated across the machine learning fairness community, in public policy research, privacy and data protection studies, and critical data scholarship. Yet, there is still no consensus on what constitutes ‘high-quality’ synthetic data. Against this background, I demonstrate how the concept of synthetic data introduces an analogical perspective on data. This perspective is relational and regulative, extending the discussion on data quality to encompass questions of data justice and responsible innovation. It invites critical reflections on the purpose and trade-offs involved in synthetic data generation and use, the social practices and power dynamics that underpin and configure it, and how its direction can be shaped in response to changing real-world circumstances and emerging human values. Building on this analysis, I argue that the generation and use of meaningful synthetic data require promoting responsibility in complex AI and data innovation ecosystems, and facilitating forms of algorithmic reparation and responsiveness. ...

2026年9月25日 · 2 分钟 · Marianna Capasso(荷兰乌得勒支大学 Utrecht University)

Same goal, different paths: Contrasting approaches to AI regulation in China and India

摘要(出版方所载原文摘要) This paper is a comparative analysis of how two leading developing nations, China and India, are proposing to regulate artificial intelligence (AI) systems. Despite similarity in circumstances as large developing economies aiming to upgrade their technology sectors and create jobs, the two countries have taken significantly different approaches to AI regulation. Based on interest group theory, we argue that contrasting problem definitions—predominantly in terms of economic competitiveness and national security in China and as applications in India—resulted in the recruitment of very different decision-making groups in the two countries; homogeneous groups of technocrats and security specialists in China and a broader group including consumer advocates in India. This in turn resulted in ambitious and deep policy changes in China and relatively incremental and consensus-based moves in India. ...

2026年9月25日 · 1 分钟 · Puxin Zhang、Krishna Jayakar、Richard D. Taylor、Chun Liu(美国宾夕法尼亚州立大学等)