Methodology
Ethics model covering consent, purpose limitation, fairness, transparency and harm avoidance with a review mechanism.
Components
Ethical principles; Consent & purpose; Fairness & non-discrimination; Transparency; Harm assessment; Ethics review.
Governance
Ethics committee owns; data owners apply; governance council oversees. 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
Data-ethics programme; Ethics review build.
Across the Data & AI ecosystem
Knowledge graph · 2 relations