What would a fairer collective AI be worth to you?
This calculator starts from your figures (volume, miss rate, cost of a missed case, targeted reduction) and estimates the potential annual value of a fairer model. Mesh adds no figure: the reduction you enter is a hypothesis to validate on your data in a pilot, not a promised result.
What costs the most: false negatives
The missed case (the undetected defect, the fraud that slips through, the invisible intrusion) costs far more than its direct value, through its knock-on effects: production-line stoppage, recall, litigation. That is exactly where a model trained by the collective catches up the most : by crossing the signals of several actors, it sees what a lone actor missed.
demo · synthetic data In our maintenance scenario, on a machine that does fail, a single actor's model rates the risk at 16.8 % (“nothing to report”, it misses it), while the network model rates it at 94.3 % (danger, it catches it). This sector scenario runs on synthetic data, at a fixed seed: it illustrates a mechanism, it is not a customer result, and it is to be confirmed on your data in a pilot.
The method, in plain terms
The value at stake is computed from four quantities, all specific to your organization:
Annual value ≈ Volume × Miss rate × Targeted reduction × Unit cost
The only uncertain term is the targeted reduction: the gain a model trained by the collective could bring by using signals a lone actor cannot see. Nobody can guarantee it upfront: it is measured in a pilot, on your real data, before any commitment. That is why this calculator suggests no default gain figure : you set the hypothesis, we verify it together.
This value model applies to detection use cases (maintenance, fraud, medical risk, cybersecurity, quality control): wherever a missed case has a cost. Forecasting cases (estimating a continuous value, e.g. energy output or a footprint) call for a different value model, framed with you case by case.
Frequently asked questions
How do you estimate the ROI of a federated learning project?
Start from your figures (annual volume, current miss rate, cost of a missed case, targeted reduction) and apply the formula above. The targeted reduction is a hypothesis to validate in a pilot, not a guaranteed result.
Does federated learning really improve performance?
By pooling learning without sharing data, a model can use signals a lone actor cannot see. The size of the gain depends on the data and the use case, and is measured in a pilot on each participant's real data.
What does a Mesh project cost?
A one-time setup and then an annual licence per member, from three actors onward. The amount is discussed case by case and compared against the value estimated on this page.
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