PREPARE YOUR DATA / DA-04

Data Fitness

0.28Adaptability average

Data fitness is distinct from general data quality. General quality asks whether data is accurate, complete and consistent; fitness asks whether a specific dataset has the right features, historical depth and representativeness for a specific AI use case — the same dataset can be fit for one model and unfit for another, since fraud detection, demand forecasting and bias auditing each impose different requirements. MIT CISR recommends dedicated fitness review processes — specialist teams that evaluate datasets against each proposed use case, with formal sign-off before model training begins — on the assumption that fitness is a technical assessment best performed by people with deep statistical and ML expertise.

Potential across the 5 Future Positive Principles

Self-Directed

Agency-Centered

Impact-Led

System-Focused

Evolution-Driven

Industry Standard baseline

Future Positive potential

Source Frameworks