It’s infuriatingly hard to understand how closed models train on their input
The widespread anxiety regarding large language models memorizing private data stems primarily from the lack of transparency in closed-source AI development. Because vendors like OpenAI and Google withhold specifics about their training datasets, users cannot verify whether inputs provided via ChatGPT or APIs are repurposed for future model iterations. This opacity prevents definitive reassurance, as the exact mechanisms for data retention and fine-tuning remain ambiguous, leaving the possibility that private information could be inadvertently incorporated into subsequent model versions despite published policies. While OpenAI claims API data is not used for training, the practices surrounding consumer-facing tools like ChatGPT remain unclear. Historical documentation suggests data may be used for reinforcement learning from human feedback rather than direct raw input training, yet the lack of clarity on whether personal identifiable information is fully scrubbed sustains user distrust. Furthermore, security vulnerabilities in these platforms pose an additional risk, as logged data could be exposed through system flaws or insider threats, highlighting the immature security infrastructure of newer AI providers compared to established cloud services. This uncertainty underscores the critical relevance of open data and open-source models in the AI ecosystem. Self-hosting openly licensed models allows organizations to bypass proprietary training concerns entirely, ensuring that sensitive data never leaves their control. As these community-driven models rapidly improve in capability, they offer a viable alternative for enterprises seeking to leverage AI without the privacy risks inherent in closed systems, making transparency and local deployment essential for trustworthy AI adoption.
Source: simonwillison.netPublished on 2023-06-05