Training Data Freshness & Bias
The data a model was trained on doesn't stop aging the day it goes to production. This plan watches for when it stops representing the real world, and what biases it started with.
What's included
- PDF with the freshness monitoring plan: which indicators to review and how often, depending on the domain's volatility
- Bias and representativeness checklist: checks on demographic groups, time periods, and over/under-represented sources
- Excel with a per-dataset log template
- Legal Notice
Why this document exists
AI Act Art. 10 requires training data to be "relevant, sufficiently representative and, to the best extent possible, free of errors" — but that assessment can't be done only once, when building the model, and considered settled forever. Data a model was trained on two years ago describes a world that has already changed, and the representativeness it had at the start may have eroded without anyone noticing until a problem shows up in production.
Frequently asked questions
How is this different from the AI Act Art. 10 Compliance Pack?
The Art. 10 Pack covers the data governance documentation required when building the system. This product is the ongoing follow-up: watching whether that data stays representative and current over time.
What does it mean for a training dataset to lose "freshness"?
It means the world that data describes no longer matches the current world. A model trained on 3-year-old data may be making decisions based on a reality that no longer exists.
What format is it delivered in?
PDF with the freshness monitoring plan and the bias/representativeness checklist, plus Excel with a per-dataset log template.