Accountability & oversight
Named owners, decision rights, executive accountability, escalation, and independent challenge.
Research framework / Responsible AI
Responsible AI as an operating system.
A research-led framework for translating principles into accountable decisions, measurable controls, durable evidence, runtime oversight, and continuous assurance.
01 / ScopeInstitutional
Enterprise, public-sector, and regulated AI systems.
02 / UnitSystem + use case
Govern the deployed context, not the model in isolation.
03 / EvidenceRequired
Controls should produce reviewable artifacts and records.
04 / LifecycleContinuous
Reassess as capability, context, data, and risk change.
Principles matter. Operating evidence makes them governable.
Responsible AI becomes institutional when accountability, risk decisions, technical controls, human oversight, security, and monitoring are connected to evidence that can be reviewed and challenged.
IdentifyClassifyAssignAssessControlTestApproveObserveRespondReassess
Control domains
Eight domains for responsible deployment.
The framework is designed as a research architecture that institutions can map to their own policies, risk models, standards, and regulatory obligations.
Use-case & risk classification
Purpose, affected stakeholders, autonomy, criticality, data sensitivity, and potential impact.
Data & model integrity
Data provenance, model selection, evaluation, limitations, versioning, and change control.
Human agency & safeguards
Approval thresholds, override, review, escalation, contestability, and safe fallback paths.
Security & identity
Identity, least privilege, credential control, tool boundaries, secrets, logging, and revocation.
Transparency & records
System documentation, user disclosure where appropriate, decision records, and traceability.
Monitoring & incidents
Operational telemetry, drift and anomaly signals, incident handling, remediation, and lessons learned.
Assurance & reassessment
Control testing, evidence review, exceptions, periodic reassessment, and governance versioning.
Evidence model
Every material control should leave a trace.
Evidence can include inventory records, risk classifications, model and use-case evaluations, approval records, policy checks, access logs, testing results, incident records, remediation actions, monitoring signals, and reassessment decisions.
The objective is not documentation for its own sake. The objective is to make governance observable enough that an independent reviewer can determine what was required, what occurred, what evidence exists, who approved it, and what remains unresolved.
Research architecture
From governance intent to defensible assurance.
Five layers connect institutional policy to operational evidence.
01Policy & obligations
Institutional principles, sector requirements, legal obligations, and internal policy.
02Control design
Concrete control objectives, accountable owners, thresholds, and expected evidence.
03Operational execution
Reviews, technical safeguards, approvals, human interventions, and monitoring.
04Evidence & challenge
Artifacts, logs, test results, exceptions, independent review, and documented challenge.
05Assurance & change
Findings, remediation, residual risk decisions, reassessment, and versioned governance.
External research reference
Evidence-led governance research.
This framework is informed by broader research into operational AI governance and assurance. AIGX Research™ publishes related work on evidence completeness, sector frameworks, governance controls, ratings methodology, and responsible AI assurance.
Visit AIGX ResearchThird-party reference for research context. Reference does not imply affiliation, endorsement, sponsorship, certification, or regulatory approval.
Research use
A framework for analysis—not a certification.
The London AI Research™ Responsible AI Framework is a research instrument. It is not legal advice, a compliance determination, regulatory approval, audit opinion, or certification.
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