icarm - Interpretable Contextual-Accountable and Responsible Machine
Learning
A general-purpose framework for Interpretable
Contextual-Accountable and Responsible Machine Learning (ICARM)
that works with any clean tabular data across any application
domain including healthcare, finance, social science, business,
and education. Automatically detects whether a prediction task
is binary classification, multi-class classification, or
regression from the target variable type. Provides a unified
entry point icarm_fit() supporting both interpretable learners
(CART, logistic regression, linear regression, GAM) and
extended learners (random forest, XGBoost, SVM) with consistent
interfaces for global and local model explanation, group-level
fairness auditing across protected attributes, probability
calibration, threshold analysis, multi-model comparison,
reproducible JSON audit trails, and accountability scorecards.
The contextual accountability framing emphasises that
algorithmic fairness and interpretability requirements depend
on the deployment domain and must be evaluated accordingly.
Extends the civic.icarm framework (Awe 2025)
<https://cran.r-project.org/package=civic.icarm> to
general-purpose applications beyond civic and political
education.
