Editorial research · Taipei
Granite Pilot Insights reads the reasoning behind AI-assisted investment decisions
Granite Pilot Insights publishes free, independent articles on the AI models that shape investment decisions — what feature attribution, model cards and interpretable surrogates can tell a careful reader, and where they quietly stop being honest. Nothing is sold here. Inquiries only.
A reader's checklist
What an explainable investment model should be able to show you
Before trusting an AI signal in an investment decision, we read it against a short list of questions. These four marks frame most of our articles.
It names the features that moved the prediction
Explainability begins with attribution. A model that returns a portfolio tilt without saying which inputs drove it is not yet an explainable model — it is a black box with a confidence score attached. We cover techniques such as SHAP, integrated gradients and permutation importance, and the ways each one can be gamed by correlated inputs.
It carries a model card, not a slogan
Model cards and fact sheets document training data, intended use, known failure modes and performance by subgroup. For investment models, that means describing the market regime the model was trained on and where it degraded out-of-sample. We read these disclosures closely, and we write about the ones that are missing.
It survives a simpler surrogate
If a shallow tree can mimic a deep ensemble on the same inputs, the complex model was carrying little extra signal. If it cannot, the gap is worth explaining. We write about interpretable surrogates, partial dependence plots and the honest limits of approximating a model you cannot fully open.
How we read the evidence
From attribution scores to a defensible investment thesis
Explainability is not a single output; it is a chain of evidence. A SHAP value tells you which features mattered for one prediction. A model card tells you the conditions under which that prediction should be trusted. A backtest under a different market regime tells you whether the explanation still holds when the world changes.
Our articles follow that chain rather than stopping at the first link. We trace how an investment model's reasoning is documented, how its explanations behave under stress, and what a portfolio manager can responsibly say to a client when a position was chosen by a model the manager cannot fully open.
- Attribution methods and the ways correlated inputs distort them
- Model cards and the disclosures investment systems often omit
- Surrogate models, partial dependence and where approximation breaks down
- Regime change, out-of-sample decay and explanation drift
Editorial independence
No funds managed, no signals sold, no share of any trade taken
Granite Pilot Insights is an editorial project. We publish articles about explainable AI in investment decisions and we answer reader inquiries. Nothing here is for sale: no subscription, no premium tier, no managed account, no brokerage. The only thing we ask for is a clearly written question.
Who this is for
A short checklist before you send an inquiry
We answer inquiries that fit what we actually do. The two lists below save everyone time.
Useful to ask us
- How a specific attribution method behaves under correlated inputs
- What a model card for an investment system should disclose
- How to read an explanation that survives a regime change
- Where an interpretability claim outruns its evidence
Not something we do
- We do not act as an investment adviser
- We do not place trades on a client's behalf
- We do not hold client funds or deposits
- We do not sell access to signals, models or data feeds
What you get back
- A written reply from the editorial team by email
- A pointer to the relevant article when one exists
- An honest note when a question falls outside our scope
- No sales follow-up, because there is nothing to sell