civic.icarm

civic.icarm provides a unified, pedagogically-grounded R framework for Interpretable, Civic-Accountable, and Responsible Machine Learning (ICARM).

It is the computational backbone of the DataCitizen-Pro project a proposed DFG-funded research programme at Ludwigsburg University of Education (LUE) developing data literacy, statistical reasoning, and democratic judgment in civic and statistical education.

“Algorithmic decisions that affect civic life must be interpretable, auditable, and fair - not merely accurate.” DataCitizen-Pro, DFG Sachbeihilfe 2026

Installation

# From CRAN (once accepted)
install.packages("civic.icarm")

# Development version from GitHub
remotes::install_github("Olawaleawe/civic.icarm")

Quickstart

library(civic.icarm)

# Works with ANY tabular data - task auto-detected
m <- civic_fit(voted ~ ., data = civic_voting)

# Explain
ex <- civic_explain(m, data = civic_voting)
civic_plot_importance(ex)

# Fairness audit
fair <- civic_fairness(m, civic_voting,
                       outcome   = "voted",
                       protected = "gender",
                       positive  = "yes")
civic_plot_fairness(fair, metric = "tpr")

# Full accountability scorecard
civic_scorecard(m, civic_voting,
                outcome   = "voted",
                protected = "gender",
                positive  = "yes",
                project   = "DataCitizen-Pro")

Key functions

Function	Description
`civic_fit()`	Train any model - auto-detects binary, multiclass, regression
`civic_explain()`	Global feature importance
`civic_fairness()`	Group equity metrics across protected attributes
`civic_calibrate()`	Probability calibration diagnostics
`civic_compare()`	Side-by-side multi-model comparison
`civic_audit()`	Reproducible JSON audit trail
`civic_scorecard()`	Full civic accountability report

DataCitizen-Pro connection

Competency pillar	civic.icarm module
Data Literacy	`civic_fit()`, `civic_audit()`
Statistical Reasoning	`civic_metrics()`, `civic_thresholds()`, `civic_calibrate()`
Democratic Judgment	`civic_fairness()`, `civic_scorecard()`

Built-in datasets

Dataset	Rows	Task
`civic_voting`	1,000	Binary classification
`civic_education`	800	Regression
`civic_german_credit`	1,000	Binary classification (fairness benchmark)

Author

Prof. Dr. Olushina Olawale Awe Alexander von Humboldt Foundation Visiting Professor Statistical and Data Science Literacy Ludwigsburg University of Education (LUE), Germany olawaleawe@gmail.com

Citation

@software{awe2026civicicarm,
  author = {Awe, Olushina Olawale},
  title  = {{civic.icarm}: Interpretable, Civic-Accountable and
            Responsible Machine Learning},
  year   = {2025},
  url    = {https://github.com/Olawaleawe/civic.icarm},
  note   = {R package v0.2.0. DataCitizen-Pro DFG Sachbeihilfe,
            Ludwigsburg University of Education.}
}

Acknowledgements

Developed within the DataCitizen-Pro project submitted to the Deutsche Forschungsgemeinschaft (DFG) Sachbeihilfe programme. The Alexander von Humboldt Foundation is thanked for supporting the Visiting Professorship at LUE.