This folder contains the evaluation artifacts for RGDS (Regulated Gate Decision Support).
Together, these files define how decision quality and governance effectiveness are assessed in a regulated, human-governed decision-support system.
These artifacts are aligned to the v2.0.0 whitepaper-defined governance baseline, including mandatory options analysis, evidence completeness classification, residual risk capture, named human accountability, and bounded AI disclosure.
The artifacts are intentionally layered.
They should be read in sequence, not as isolated documents.
File:
evaluation-note.md
Purpose:
Provides interpretive context for RGDS evaluation.
- Explains why specific evaluation measures exist
- Reinforces governance principles (e.g., AI is not evidence by default)
- Prevents misinterpretation of metrics as model benchmarking
This document is non-authoritative and conceptual.
It exists to orient readers before reviewing formal evaluation definitions.
File:
evaluation-plan.md
Purpose:
Defines how RGDS is evaluated as a human-governed decision-support system.
The evaluation plan specifies:
- evaluation scope
- decision quality dimensions
- governance and AI-use checks
- roles and responsibilities
- evaluation cadence and success criteria
This is the authoritative evaluation definition.
All other evaluation artifacts align to this plan.
File:
evidence-quality-rubric.md
Purpose:
Provides structured criteria for assessing whether evidence is fit for the decision being made.
The rubric supports:
- consistent reviewer scoring
- explicit confidence assignment
- transparent discussion of gaps and limitations
It operationalizes the “evidence quality” dimension defined in the Evaluation Plan.
File:
decision-gate-extract.md
Purpose:
Defines a flattened decision representation derived from RGDS decision logs.
This extract is designed for:
- phase-gate forums
- executive reviews
- portfolio-level analysis
It preserves traceability to:
- external requirements
- observed gaps
- backlog items
- full decision log JSON
The decision log remains the source of truth.
The Decision Gate Extract is a read-only derivative and must never be used to approve, override, or reinterpret a decision.
File:
decision-gate-extract-powerbi-sample.md
Purpose:
Provides a concrete example of how the Decision Gate Extract can be operationalized
in a BI tool (e.g., Power BI).
This sample illustrates:
- a BI-ready schema
- example transformations
- governance-focused dashboard views
- drill-through to auditable decision records
It is illustrative only and does not mandate tooling or implementation.
The following artifacts support traceability, defensibility, and repeatability across all evaluations.
File:
ind-requirements-gap-log.md
Purpose:
Documents observed gaps between external IND expectations
and common delivery practices.
Each gap:
- is assigned a stable
IND-GAP-XXXidentifier - is linked to one or more backlog items
- provides evidence-backed justification for RGDS design decisions
This artifact explains why evaluation and backlog work exists.
File:
requirements-traceability-matrix.md
Purpose:
Provides end-to-end traceability across:
External requirement
→ Observed gap
→ Backlog item
→ RGDS artifact
The RTM enables:
- governance review
- audit readiness
- confirmation that evaluation artifacts map back to real requirements
This is the primary traceability artifact for reviewers.
File:
scorecard-template.csv
Purpose:
Defines a lightweight, repeatable format for capturing
per-decision evaluation signals, including:
- reviewer confidence
- evidence completeness
- governance execution indicators
- decision outcome context
The scorecard supports:
- per-decision evaluation
- phase-level aggregation
- retrospective trend analysis
Scorecards support evaluation only; they do not constitute approval, rejection, or risk acceptance.
Evaluation Note
→ Evaluation Plan
→ Evidence Quality Rubric
→ Decision Gate Extract
→ Power BI Sample
Supported by:
→ IND Requirements Gap Log
→ Requirements Traceability Matrix
→ Scorecard Template
- The Note explains intent
- The Plan defines evaluation
- The Rubric enables scoring
- The Extract supports review
- The Sample shows operationalization
- The Gap Log & RTM ensure traceability
- The Scorecard captures execution evidence
Together, they ensure RGDS evaluation is:
- defensible
- interpretable
- auditable
- explicitly human-governed
- Evaluation artifacts are expected to evolve as RGDS matures.
- Changes should preserve traceability and backward compatibility.
- BI tooling and AI assistance must never become decision authorities.
RGDS evaluation exists to support better human decisions, not to replace them.