civic.icarm-package: civic.icarm: Interpretable Civic-Accountable and Responsible...

In civic.icarm: Interpretable Civic-Accountable and Responsible Machine Learning

civic.icarm: Interpretable Civic-Accountable and Responsible Machine Learning

Description

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v085.i04")}, Kuhn (2008) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v028.i05")}, Awe (2025) https://github.com/Olawaleawe/civic.icarm.

Author(s)

Maintainer: Olushina Olawale Awe [e-mail]

Other contributors:

• Ludwigsburg University of Education [funder]

civic.icarm documentation built on June 18, 2026, 1:06 a.m.