Formal decision-workflow (detailed ML evidence)

Below is a concise formal workflow mapping how ADRs feed governance and ML systems, with roles, gates, artifacts, ML-specific evidence (tests, dataset lineage, bias checks, thresholds), and audit outputs. After reading, please pick one element you'd like expanded or turned into a template.

Decision-workflow: Proposal → Approval → Deployment (overview)

Proposal (Authoring) — Proposer (Engineer / Data Scientist) creates an ADR draft that includes problem statement, options considered, recommended option, rationale, risks, rollback plan, and required evidence checklist specific to ML (dataset lineage, metrics, tests, explainability).
Preliminary Review — Team Lead/Tech Lead reviews for technical completeness; requests missing artifacts. ADR linked to model metadata in model registry and to a draft ticket in governance tracker.
Architecture Board Gate — Architecture Board evaluates system impact, scalability, integration concerns. Required artifacts: ADR, system diagrams, integration tests, CI/CD impact analysis. Approval recorded in architecture board minutes and linked artifact store.
Governance & Compliance Gate — Governance Committee (legal, security, compliance) assesses privacy, regulatory, and ethical risks. Required artifacts: privacy impact assessment, bias assessment, explainability summary, data retention plan. Approval requires sign-off; conditions recorded.
Pre-Deployment Checks — Ops/SRE and ML Engineers run automated checks: unit/integration tests, model performance regression tests, canary test plan, threshold checks, and monitoring config. Evidence captured in CI/CD job outputs and ML metadata.
Deployment & Monitoring — Deploy to production per CD pipeline. Model and ADR links included in deployment manifest. Monitoring pipelines record drift metrics, performance, and trigger alerts. Audit logs capture approvals, artifact versions, and deployment events.
Post-Deployment Review — Stakeholders (Product, Data Science, Compliance) conduct scheduled review (e.g., 30 days) to validate assumptions, update ADR if outcomes differ, and document lessons learned.

Roles & Responsibilities

Proposer: authors ADR, compiles ML evidence, links artifacts.
Engineers / Data Scientists: provide tests, dataset lineage, model artifacts.
Tech Lead / Architects: technical review, system impact assessments.
Architecture Board: cross-team approval for architecture concerns.
Governance Committee: compliance, legal, security approval and conditions.
Ops / SRE: validate deployment readiness and monitoring.
Product / Business Leaders: business acceptability and KPIs.
End-user reps / UX: user impact and usability concerns.

ML-specific required evidence (detailed)

Dataset lineage: source systems, extraction code, preprocessing steps, snapshot hashes, retention policy.
Training/Validation/Test splits and sampling strategy; versioned dataset identifiers.
Evaluation metrics: primary metric(s) with baselines and confidence intervals, per-segment breakdowns.
Bias & fairness checks: group-wise performance, disparate impact metrics, mitigation steps and validation.
Robustness tests: adversarial tests, input perturbation tests, distribution shift simulations.
Explainability artifacts: model cards / SHAP/LIME summaries, decision rules, feature importance reports.
Threshold & rollback rules: decision thresholds, SLA targets, automated rollback triggers.
CI/CD test outputs: unit/integration test logs, model validation job artifacts, reproducible pipeline run IDs.

Artifacts & Integrations

ADR stored in architecture docs repo (versioned), linked to the model registry entry and ticketing system.
Model registry stores model binary, metadata (training code commit, dataset versions, metrics), and pointers to explainability reports.
CI/CD pipeline enforces pre-deploy checks and records outputs in audit logs.
Governance tracker records approvals, conditions, and required remediation actions; snapshots are archived for audits.
Audit logs capture who approved what, artifact hashes, timestamps, and deployment manifests.

Decision Gates & Evidence Requirements (templates)

Gate: Architecture Board — Required: ADR + system diagram + integration test results + CI impact statement.
Gate: Governance Committee — Required: Privacy impact, bias assessment, legal sign-off, explainability summary.
Gate: Pre-deploy — Required: Passing CI/CD test suite, model validation job, threshold checks, monitoring config.

Audit & Traceability

Every approval action writes an immutable audit entry: approver ID, role, artifact versions (hashes), timestamp, and comments.
ADR revisions are versioned; each revision references the model registry version and CI/CD run IDs used as evidence.
Retention policy: store ADRs and linked artifacts for compliance window (e.g., 7 years) or per-regulatory requirement.

Which element would you like expanded into a template or example? (pick one)

Choose one

ADR template (fields + short instructions)
Architecture & Governance gate checklists (detailed)
ML evidence template (dataset lineage, tests, bias checks)
Mermaid workflow diagram (formal flow with roles)

Optional: one short constraint or format preference

Learning: Decision structures