published
Complete Version 1.0 master document.
Ethics
Turn responsible AI principles into decision rights, safeguards, evidence and accountable practice.
Dimension version v1.0
Definition
Turn responsible AI principles into decision rights, safeguards, evidence and accountable practice.
Contents
The complete document structure, including its introduction, chapters and closing sections.
—Introduction and source information—E – EthicsThe Ethics dimension examines the organizational conditions through which AI can be used in ways that are legitimate, accountable, evidence-based and responsive to affected people. It connects intent and obligation to the decisions, controls, tests, operating behaviors and remedies that shape real outcomes.—Guidance for AssessorsAssess Ethics through observable decisions and operating evidence, not declared intent alone. Start with the real decision, action or distribution of power created by the AI-enabled system, then trace representative cases through purpose, classification, controls, approval, monitoring, material change, incident or complaint handling, recourse and s—Assurance chain and ownershipFigure E-2. The Ethics assurance chain turns commitments into decisions and returns operational evidence to governance.E.1E.1 Ethical Intent, Human Rights and AccountabilityEthical intent examines whether the organization can determine not only whether an AI use case is technically possible, but whether it is legitimate in the context in which it will operate. The focus is the real decision, action or redistribution of power created by the system, together with the people who may experience its benefits, burdens or haE.2E.2 Risk-Based Governance, Decision Rights and Lifecycle AssuranceRisk-based governance examines whether the organization can direct the right degree of scrutiny to each AI system and agent throughout its lifecycle. The purpose is not to create the same process for every use case, but to connect consequence, uncertainty, scale and autonomy to proportionate decision rights, evidence and intervention.E.3E.3 Responsible-AI Requirements and Enforceable GuardrailsResponsible-AI requirements examine whether values, duties and risk decisions are converted into controls that teams can implement, test and improve. The focus is not the existence of principles alone, but the path from each material commitment to an applicable requirement, a control owner, a test result and current evidence.E.4E.4 Transparency, Traceability, Explainability and RecourseTransparency examines whether different stakeholders receive the information they need to understand AI involvement, use a system safely, exercise oversight or challenge a consequential outcome. It is therefore designed around audience and decision need rather than around the volume of technical information published.E.5E.5 Agentic-AI Autonomy, Identity and Human ControlAgentic AI changes the governance object from an output-producing model to a digital actor that may plan, call tools, modify state, retain memory and delegate work. This subdimension examines whether that action remains attributable, explicitly authorized, bounded and recoverable as autonomy increases.E.6E.6 Regulatory Readiness, Assurance and Responsible ScaleRegulatory readiness examines whether the organization can translate changing obligations into operating decisions and credible evidence. Compliance remains a minimum condition rather than the full definition of ethics, but fragmented duties, unclear organizational roles and unsupported claims are practical sources of transformation failure.—Dimension Synthesis and Transformation RoadmapThe six Ethics subdimensions form one governance and assurance operating system. Ethical intent establishes why and under which boundaries AI may be used; risk governance directs scrutiny; requirements and guardrails translate commitments into control; transparency and recourse keep outcomes inspectable and challengeable; agentic control bounds del—From evidence to transformation decisionFor each capability, distinguish design intent from operating proof. Record evidence freshness, scope, independence, limitations and uncertainty.—Standards and Regulatory Reference PointsThe Ethics dimension is designed to remain useful across changing technologies and jurisdictions. The following sources inform its control, risk, impact, management-system and agentic-assurance concepts. Applicability and legal interpretation must be determined for the organization’s role, sector, jurisdiction and use context; this list is not lega—How to use these references• Use law to determine binding duties, roles, dates, prohibitions and required evidence.—Source headers, footers and notes—Practices & playbooksThe practical companion documents linked to this section.—Version historyChanges to this document, separate from the overall framework release history.
Related practices
PlaybookGuidance
Introduction and source information
This document converts the AIDIVE Ethics dimension into repeatable consulting and operating practices. It is intended for transformation teams, product and service owners, risk and assurance functions, platform teams and leaders who need to make responsible-AI decisions at delivery speed.
Ethics
AR-E01
AR-E01 · Ethical Charter and Non-Negotiables Workshop
Turn enterprise values, duties and risk appetite into a short decision charter that teams can apply to real AI use cases and agent actions.
Ethics
AR-E02
AR-E02 · Stakeholder and Human-Rights Impact Assessment
Identify and manage actual, potential and cumulative impacts on people, groups, society and the environment across the AI lifecycle.
Ethics
AR-E03
AR-E03 · Non-Delegable Decision and Accountability Design
Within the established decision architecture, make human outcome ownership, non-delegable decisions and the boundaries of AI or agent delegation explicit in the real workflow.
Ethics
AR-E04
AR-E04 · Contestability, Recourse and Remedy Design
Give affected people a practical way to understand, challenge, correct and obtain review or remedy for AI-influenced outcomes.
Ethics
AR-E05
AR-E05 · AI and Agent Inventory with Risk Classification
Create a living, decision-useful inventory and assign proportionate inherent and residual risk tiers to systems and agents.
Ethics
AR-E06
AR-E06 · Lifecycle Ethics and Risk Gate Design
Embed proportionate evidence and real decision authority into discovery, design, release, change, scale and retirement.
Ethics
AR-E07
AR-E07 · Decision Rights and Independent Challenge Map
Within the established decision architecture, clarify the Ethics-specific authorities for residual-risk acceptance, exceptions, independent challenge, restriction, intervention, shutdown, restart and assurance.
Ethics
AR-E08
AR-E08 · Continuous Monitoring and Material-Change Review
Connect production signals and material changes to timely investigation, control adjustment, restriction or reassessment.
Ethics
AR-E09
AR-E09 · AI Incident, Near-Miss and Harm Response
Contain harm, preserve evidence, protect affected people, learn from failure and control safe restart.
Ethics
AR-E10
AR-E10 · Principles-to-Controls Traceability
Connect ethical principles and obligations to system requirements, implemented controls, tests, evidence and exceptions.
Ethics
AR-E11
AR-E11 · Guardrail Design and Control Validation
Design layered safeguards at the points where AI output or agent action can still be prevented, constrained, detected or corrected.
Ethics
AR-E12
AR-E12 · Fairness and Differential-Impact Test
Define context-appropriate fairness objectives and test unequal errors, allocation, access, workload, override and downstream impact.
Ethics
AR-E13
AR-E13 · Privacy, Security and Data-Use Boundary Review
Review whether privacy, security and data-use boundaries across prompts, retrieval, tools, logs, memory and generated outputs are appropriate to the approved purpose and materially protect affected people and the organization.
Ethics
AR-E14
AR-E14 · Third-Party and Foundation-Model Assurance
Evaluate which provider evidence and controls can be relied on, which obligations remain with the provider and what must be established for the organization’s own deployment context and dependencies.
Ethics
AR-E15
AR-E15 · System, Model and Agent Transparency Card
Maintain a concise, versioned description of the deployed AI system, its dependencies, limits, controls, oversight and current evidence.
Ethics
AR-E16
AR-E16 · Decision Traceability and Audit Reconstruction
Ensure material AI-supported outcomes and agent actions can be reconstructed with attributable authority, evidence, versions and controls.
Ethics
AR-E17
AR-E17 · Stakeholder Disclosure and Explanation Design
Provide timely, accessible and faithful information that enables safe use, informed choice, oversight and challenge.
Ethics
AR-E18
AR-E18 · Content Provenance and Information-Integrity Control
Preserve source, generation, alteration, approval and publication evidence for content where deception, IP or public-trust risk is material.
Ethics
AR-E19
AR-E19 · Agent Autonomy and Delegated-Authority Design
Within the approved Autonomy Appetite and decision authority, define an agent’s permitted actions as an explicit, time- and scope-bounded authority envelope that remains attributable to an accountable human or service owner.
Ethics
AR-E20
AR-E20 · Agent Identity, Permission and Tool-Boundary Control
Give agents attributable identity and least-privilege, action-aware access to tools, data and destinations.
Ethics
AR-E21
AR-E21 · Human Oversight and Intervention Effectiveness Test
Verify that people can recognize, evaluate, interrupt, override and safely take back agent or AI work under real operating pressure.
Ethics
AR-E22
AR-E22 · Multi-Agent Supervision, Memory and Termination Review
Review whether multi-agent arrangements preserve authority boundaries, supervision, memory limits and safe termination across handoffs, long-running work and compound failure conditions.
Ethics
AR-E23
AR-E23 · Regulatory Applicability and Obligations Register
Translate changing legal, sector and contractual duties into system-specific controls, evidence, notices, owners and dates.
Ethics
AR-E24
AR-E24 · AI Management System and Assurance Evidence Pack
Build a coherent, evidence-based assurance case that connects purpose, obligations, impacts, risks, controls, evaluation, decisions and improvement.
Ethics
AR-E25
AR-E25 · Responsible Scale and Regulatory-Horizon Review
Decide whether controls and evidence remain credible as AI moves to new populations, geographies, channels, suppliers, tools or autonomy.
Ethics
PlaybookGuidance
Operating Rhythms
Practices create Ethics capability; operating rhythms keep that capability visible, current and responsive to evidence over time. AR-R22–AR-R26 are Ethics decision and assurance patterns designed to operate through the organization’s existing delivery, lifecycle, agentic-oversight, incident/recourse and scale and regulatory governance cadences. The
Ethics
AR-R22Rhythm
AR-R22 · Weekly Ethics Control Triage
Ethics
AR-R23Rhythm
AR-R23 · Biweekly Lifecycle Risk and Change Conversation
Ethics
AR-R24Rhythm
AR-R24 · Monthly Agentic-AI Oversight Conversation
Ethics
AR-R25Rhythm
AR-R25 · Monthly Incident, Recourse and Learning Conversation
Ethics
AR-R26Rhythm
AR-R26 · Quarterly Responsible Scale and Regulatory Conversation
• Each rhythm should produce a named decision, evidence update, accountable owner and follow-through date.
Ethics
PlaybookTemplate
Minimum Consulting Templates
The following templates define the minimum reusable artifacts for applying the Ethics practices consistently. They may be implemented in documents, workflow systems, assurance records or delivery platforms as long as the required fields and decision intent are preserved. Templates should make a decision easier, not become evidence by existence; kee
Ethics
PlaybookGuidance
Consultant Application Guidance
Place the current Ethics capability level according to evidence demonstrated consistently across the relevant scope, not the most mature isolated example.
Ethics
PlaybookGuidance
Source headers, footers and notes
Ethics
Dimension version history
This history belongs to Ethics. Overall framework releases are recorded separately in Framework release history.