Data Privacy and AI Progress
https://www.theregreview.org/2026/05/26/frazier-glover-data-privacy-and-ai-progress/
Publish Date: 2026-05-26 00:17:00
Source Domain: www.theregreview.org
To improve AI performance, regulators must loosen restrictions on data sharing.
Every law reflects a cost benefit analysis made at the time of enactment. With respect to many privacy laws, that analysis is out of date. The costs of collecting, storing, and analyzing data in sensitive contexts, such as in health care and educational settings, once vastly outweighed the benefits. In the 1970s, for example, it likely did not make sense to collect any more information than was necessary to address a patient’s immediate needs, let alone to store that information for very long, due to high costs. The risks of that information being stolen or leaked were much graver than any positive outcomes. The same is not true today.
Advances in artificial intelligence (AI) have changed the math. It is now the case that sharing is caring—caring for your future self as well as caring for the wellbeing of others. For example, data collected during a patient’s youth may drastically improve diagnoses in adulthood. Moreover, that data may also train and improve models that result in better treatment for others. Those gains, however, will not occur if people continue to see data as something to hoard rather than to share. Similarly, if outdated laws remain on the books, then AI progress will be delayed, leading to lives being lost and educational gains going unrealized.
Of course, there are still costs to permitting more liberal data collection in sensitive domains. But those costs must be put in the context of a health care system in which medical errors run rampant and an education system in which many kids slip through the cracks. AI will not be able to address those shortcomings absent legal reforms and cultural shifts in how we think about data sharing.
That is because data are the basis on which AI systems learn what counts as a pattern and what counts as noise. The modern literature on machine learning has shown, with striking consistency, that model performance improves as…
