Responsible AI Framework

Methodology

Principles-to-practice model translating fairness, transparency, accountability, privacy and safety into design and review controls.

Components

RAI principles; Fairness & bias; Transparency & explainability; Accountability & oversight; Privacy & safety; RAI review gates.

Governance

AI governance committee owns; ethics review panel; model owners apply controls. L1 Initial L2 Developing L3 Defined L4 Managed L5 Optimised Ad hoc Basic, siloed Standardised & Quantified & integrated Predictive & embedded governed

Maturity levels

Implementation roadmap

Diagnose (assess maturity) → Design (tailor framework & governance) → Build (policies, standards, controls, pipelines) → Embed (training, culture, adoption) → Assure (test, benchmark, re-score).

Deliverables

Framework document, governance & RACI, policy/standard templates, maturity score & roadmap, board reporting pack.

Advisory opportunities

Responsible-AI programme; Ethics review build; Explainability enablement.