Training Data Transparency: What AB 2013 Means for Your MedTech Startup
The 'Black Box' is opening. California now requires you to show your training data. 📦
The Requirement
AB 2013 is a game-changer for AI transparency. It requires developers of Generative AI systems to post a summary of the datasets used to train their models. This includes:
- The sources of the data (e.g., "clinical trials," "EHR data").
- How the data was processed and cleaned.
- Whether the data includes personal information.
- Whether the data includes copyrighted material.
Competitive Risk
For MedTech startups, your data is your IP. You don't want to give away your competitive advantage. The key is to be descriptive but not exhaustive. You need to satisfy the regulator without handing your roadmap to your competitors.
Patient Privacy
Ensure your description doesn't inadvertently reveal that you used data you shouldn't have. For example, if you say you trained on "patient data from Hospital X," make sure you have a BAA (Business Associate Agreement) and patient consent that allows for that use.
Conclusion
Start documenting your data provenance now. 2026 is closer than you think, and retroactively mapping your data sources is a nightmare.
Generate Your AB 2013 Summary Faster
Use our free tool to categorize your datasets and generate a legally-compliant transparency notice in minutes.
Try the Free AB 2013 ToolFrequently Asked Questions (FAQ)
Do I have to list every file?
No. The law asks for a "summary." You can group data by category (e.g., "Medical Imaging," "Clinical Notes").
What if I use a third-party model?
If you use a third-party model (like GPT-4) without modification, you can likely point to their disclosure. But if you fine-tune it with your own data, you must disclose your fine-tuning dataset.
Is this information public?
Yes. You must post it on your website. Anyone, including your competitors and patients, can read it.