Opportunities and Challenges of Frontier Data Governance With Synthetic Data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916771397632000 |
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| author | Thakur, Madhavendra Hausenloy, Jason |
| author_facet | Thakur, Madhavendra Hausenloy, Jason |
| contents | Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance and accountability challenges, and potentially debases existing governance paradigms, such as compute and data governance. In this paper, we identify 3 key governance and accountability challenges that synthetic data poses - it can enable the increased emergence of malicious actors, spontaneous biases and value drift. We thus craft 3 technical mechanisms to address these specific challenges, finding applications for synthetic data towards adversarial training, bias mitigation and value reinforcement. These could not only counteract the risks of synthetic data, but serve as critical levers for governance of the frontier in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Opportunities and Challenges of Frontier Data Governance With Synthetic Data Thakur, Madhavendra Hausenloy, Jason Computers and Society Artificial Intelligence Machine Learning Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance and accountability challenges, and potentially debases existing governance paradigms, such as compute and data governance. In this paper, we identify 3 key governance and accountability challenges that synthetic data poses - it can enable the increased emergence of malicious actors, spontaneous biases and value drift. We thus craft 3 technical mechanisms to address these specific challenges, finding applications for synthetic data towards adversarial training, bias mitigation and value reinforcement. These could not only counteract the risks of synthetic data, but serve as critical levers for governance of the frontier in the future. |
| title | Opportunities and Challenges of Frontier Data Governance With Synthetic Data |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.17414 |