Icederived

Icederived is a term used in discussions of responsible computing to describe artifacts and processes produced under the ICED framework, which stands for Integrated Computation and Data Ethics. In this usage, icederived artifacts are designed to maintain data provenance, ensure privacy, and provide auditability across the development lifecycle.

The term emerged in academic and industry writings in the early 2020s as teams sought to emphasize ethics-by-design in tooling, models, and datasets. It is not a formal standard, but a descriptive label for outputs aligned with ICED principles.

Typical uses include icederived datasets with lineage metadata, icederived machine learning models with built-in auditing hooks, and icederived evaluation metrics that reflect privacy, fairness, and explainability constraints. Taxonomies may distinguish icederived artifacts by stages: data collection, preprocessing, model development, deployment.

Evaluation and governance: organizations implement governance to verify icederived properties, such as traceability, consent management, and compliance with regulatory frameworks. Critiques note that without consensus standards, icederived can be broad and subjective.

See also: data provenance, responsible AI, privacy-preserving machine learning, explainable AI, ICED framework.