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icarm

icarm provides a unified, general-purpose R framework for Interpretable, Contextual-Accountable and Responsible Machine Learning (ICARM) that works with any clean tabular data across any application domain.

“Algorithmic decisions must be interpretable, auditable, and fair — regardless of domain.”

What makes icarm different?

Capability	icarm	civic.icarm	DALEX	fairmodels	tidymodels
Auto-detects task type	Yes	Yes	No	No	No
Interpretable + extended models	Yes	Interpretable only	No	No	No
Random Forest / XGBoost / SVM	Yes	No	No	No	No
Group fairness metrics	Yes	Yes	No	Yes	No
Probability calibration	Yes	Yes	No	No	Yes
JSON audit trail	Yes	Yes	No	No	No
Accountability scorecard	Yes	Yes	No	No	No
General-purpose (any domain)	Yes	Civic/education focus	No	No	No

icarm is the general-purpose sister package to civic.icarm.

Installation

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

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

Quickstart

library(icarm)

# Works with ANY tabular data — task auto-detected
m <- icarm_fit(default ~ ., data = icarm_financial)

# Explain — what drives predictions?
ex <- icarm_explain(m, data = icarm_financial)
icarm_plot_importance(ex)

# Fairness audit across ethnicity
fair <- icarm_fairness(m, icarm_financial,
                       outcome   = "default",
                       protected = "ethnicity",
                       positive  = "Yes")
icarm_plot_fairness(fair, metric = "dp_ratio", ref_line = 0.8)

# Full accountability scorecard
icarm_scorecard(m, icarm_financial,
                outcome   = "default",
                protected = "ethnicity",
                positive  = "Yes",
                project   = "Loan Default Analysis")

Model family

# Interpretable (ICARM-compliant)
icarm_fit(y ~ ., data, model = "cart")        # Decision tree
icarm_fit(y ~ ., data, model = "logistic")    # Logistic regression
icarm_fit(y ~ ., data, model = "logistic_l1") # L1-penalised logistic
icarm_fit(y ~ ., data, model = "linear")      # Linear regression
icarm_fit(y ~ ., data, model = "gam")         # Generalised additive
icarm_fit(y ~ ., data, model = "multinomial") # Multinomial logistic

# Extended (post-hoc explanation recommended)
icarm_fit(y ~ ., data, model = "random_forest") # Random forest
icarm_fit(y ~ ., data, model = "xgboost")       # XGBoost
icarm_fit(y ~ ., data, model = "svm")           # Support vector machine

Built-in datasets

Dataset	Rows	Domain	Outcome	Protected attrs
`icarm_racism_survey`	150	Social science	racism_impact, migrant_status, police_stop	gender, skin_color
`icarm_medical`	500	Healthcare	readmitted (Yes/No)	gender, insurance
`icarm_financial`	1,000	Finance	default (Yes/No)	gender, ethnicity

Key functions

Function	Description
`icarm_fit()`	Train any model — auto-detects task
`icarm_split()`	Reproducible train/test split
`icarm_metrics()`	Performance metrics for any task
`icarm_explain()`	Global feature importance
`icarm_explain_local()`	Local per-observation explanation
`icarm_fairness()`	Group equity metrics
`icarm_equity_summary()`	Pass/fail fairness flags
`icarm_calibrate()`	Probability calibration (Brier, ECE)
`icarm_thresholds()`	Threshold sweep analysis
`icarm_compare()`	Side-by-side model comparison
`icarm_audit()`	Reproducible JSON audit trail
`icarm_scorecard()`	Full accountability report

civic.icarm is the civic and political education variant of icarm, focused on democratic judgment formation and DataCitizen-Pro:

install.packages("civic.icarm")

Author

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

Citation

@software{awe2025icarm,
  author = {Awe, Olushina Olawale},
  title  = {{icarm}: Interpretable, Accountable and
            Responsible Machine Learning},
  year   = {2025},
  url    = {https://github.com/Olawaleawe/icarm},
  note   = {R package v0.1.0}
}

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