Logistic regression, linear regression, generalised linear models, and survival models occupy a middle ground between heuristic systems and machine learning. They learn from data, yet their structure is transparent and their parameters directly interpretable. A logistic regression model for credit scoring produces coefficients corresponding to the contribution of each input variable to the probability of default. These coefficients can be documented, challenged, and explained to affected persons.
Statistical models are well-suited to domains with established modelling conventions and regulatory expectations, such as insurance pricing, credit risk assessment, and epidemiological modelling. Regulatory bodies in these sectors have decades of experience reviewing statistical models, and they will evaluate AI Act compliance against that baseline of expectations. The AISDP documentation for statistical models is straightforward: the model specification (equation form, feature set, coefficient values) can be presented to a qualified technical reviewer with complete transparency.
On the compliance criteria, statistical models score strongly on documentability (every parameter is a named coefficient), testability (standard evaluation methodologies are well-established), auditability (individual decisions can be reconstructed from inputs and coefficients), and determinism (outputs are fully reproducible). Bias detectability is also strong, since the model’s reliance on specific features is visible directly from the coefficients, enabling proxy variable identification. Maintainability depends on the stability of the underlying domain; models retrained on updated data typically produce predictable, incremental changes.
Where statistical models fall short is predictive performance on complex, non-linear tasks. The model selection rationale should document whether the performance gap relative to more complex alternatives is material to the system’s intended purpose and whether the compliance advantages of interpretability justify the performance trade-off.
Key outputs
- Compliance criteria scoring for statistical model candidates
- Performance comparison against more complex alternatives