Interpretable Civic-Accountable and Responsible Machine Learning R package civic.icarm version 0.3.0 A general-purpose framework for Interpretable Civic-Accountable and Responsible Machine Learning (ICARM). Works with any clean tabular data and automatically detects whether a task is binary classification, multi-class classification, or regression from the target variable type. Provides a single unified entry point civic_fit() alongside tidy interfaces for global and local model explanations, group-level fairness auditing, probability calibration, multi-model comparison, threshold analysis, and reproducible audit trails. Designed to support the DataCitizen-Pro research agenda at Ludwigsburg University of Education: developing data literacy, statistical reasoning, and democratic judgment formation in civic and political teacher education. References: Biecek (2018) <doi:10.18637/jss.v085.i04>, Kuhn (2008) <doi:10.18637/jss.v028.i05>, Awe (2025) <https://github.com/Olawaleawe/civic.icarm>. Software Depends: R (>= 4.1.0) Imports: stats, utils, rpart, ggplot2, dplyr, tidyr, tibble, purrr, rlang, jsonlite, digest Suggests: DALEX, glmnet, mgcv, pROC, nnet, testthat, covr, gridExtra Olushina Olawale Awe <olawaleawe@gmail.com> Comprehensive R Archive Network (CRAN) Olushina Olawale Awe [aut, cre], Ludwigsburg University of Education [fnd] MIT + file LICENSE (https://CRAN.R-project.org/package=civic.icarm/LICENSE) 2026-06-22 application/tgz https://CRAN.R-project.org/package=civic.icarm doi:10.32614/CRAN.package.civic.icarm en-GB