Responsible AI
Evidence Trails for Machine Learning Forecasts
What transparency can look like in a forecasting product, from model versioning and dates to ranges and evaluation records.
Machine learning outputs are easier to trust when a reader can inspect the context around them. In forecasting, transparency is less about revealing a single magic formula and more about preserving a useful evidence trail.
Record what was known
A forecast should identify when it was generated and the date of the data behind it. This prevents a later observation from being mistaken for information the model already had.
Keep the model identity attached
Model versions matter because a forecasting system changes over time. Linking an output to a version makes evaluation and comparison possible without implying that every version behaves the same way.
Preserve the distribution
Saving only a point prediction discards important information. Ranges, confidence measures, and horizon specific outputs provide a fuller record of what the system estimated.
Evaluate after the horizon closes
A forecast cannot be judged fairly before its stated horizon has elapsed. Outcome tracking should use consistent rules and should distinguish model evaluation from investment performance.
Design transparency into the interface
Methodology should not live only in a technical appendix. Dates, ranges, confidence, limitations, and links to deeper documentation should appear close to the forecast itself. Good product design turns an audit trail into something a reader can actually use.