Opportunities and Challenges of Frontier Data Governance With Synthetic Data
Track: Tiny Paper Track (between 2 and 4 pages)
Keywords: Synthetic Data, Data, AI Governance, Accountability, Trust, Regulation, Data Governance, Bias, Alignment
TL;DR: We outline 3 challenges that synthetic data poses that debase current AI governance efforts, then propose 3 technical mechanisms that address these challenges and position synthetic data as a key regulatory lever for the future.
Abstract: 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.
Submission Number: 79
Loading