IEEE 7000

Clause 5.1 establishes Concept Exploration - the foundational step where the system context + boundaries + operational environment are defined. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature on Value-Based Engineering (full IEEE text NOT reproduced): system purpose + scope + use cases + operational environment + sociotechnical context; intended users + non-users + affected parties + power dynamics + market context; technology maturity + technical constraints + organisational constraints + regulatory environment + cultural setting; identifying potential ethical concerns at concept level + sociotechnical risks; differentiation between intended and unintended uses; emergent behaviours; lifecycle stages (development + deployment + operation + decommissioning + post-decommissioning effects). The concept exploration provides foundation for subsequent value elicitation + e

• System definition + scope + boundaries documented + ConOps + OCD
• Use case + operational environment + intended/unintended uses + market context
• Sociotechnical context + affected parties + power dynamics + cultural setting analysis
• Lifecycle stage identification + development + deployment + operation + decommissioning + post-decommissioning
• Initial ethical concern register + sociotechnical risk + emergent behaviour anticipation

• System scope too narrow (only technical, missing sociotechnical)
• Use cases optimistic (no unintended use anticipation)
• Affected parties missed (only paying customers)
• Lifecycle stops at deployment (no decommissioning consideration)
• Ethical concerns only at end (no upfront surfacing)

Clauses 7 + 7.1 establish ethical requirements definition and traceability. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): translate prioritised ethical values + stakeholder concerns into system requirements; functional ethical requirements (e.g. consent mechanism + bias detection + explainability interface) + non-functional ethical requirements (e.g. fairness threshold + privacy guarantee + transparency level); requirements specified using INCOSE SE Vision + IEEE Std 29148 + INCOSE Guide for Writing Requirements; SMART criteria (Specific + Measurable + Achievable + Relevant + Time-bound) + verifiable + traceable; value-based requirements traceability matrix linking each ethical requirement to: source value + stakeholder + use case + risk + verification method + acceptance criteria. Integration with system engineering process: requir

• Ethical requirements specification + functional + non-functional + INCOSE/IEEE 29148 alignment
• Requirements per SMART + verifiable + traceable + acceptance criteria
• Value-based traceability matrix + source value + stakeholder + use case + risk + verification
• AI model validation + bias + fairness + explainability + robustness + adversarial + drift testing
• Iterative refinement throughout V-model/Agile/DevOps lifecycle + change control

• Ethical requirements aspirational (not SMART or verifiable)
• INCOSE/IEEE 29148 not applied (informal requirements)
• Traceability matrix absent (cannot link value to verification)
• AI validation only at deployment (no ongoing testing)
• Ethical requirements frozen at start (no iterative refinement)

Clauses 8 + 8.1 + 8.2 establish ethical risk management and Clause 10 establishes validation of ethical outcomes. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): ethical risk identification covering: harm to individuals + groups + communities + environment + future generations; ethical risk analysis (likelihood + severity + reversibility + scope + remediation availability); ethical risk treatment: avoid + mitigate + transfer + accept with informed consent + monitor + redesign; integration with ISO 31000 risk management + ISO/IEC 23894 AI risk management. AI risk identification and assessment: training data risks + algorithm risks + deployment risks + operational risks + monitoring risks; AI system categorization by risk level (EU AI Act prohibited + high-risk + limited risk + minimal risk; NIST AI RMF mapping); risk-tiered controls. V

• Ethical risk register + identification + analysis + ISO 31000 + ISO/IEC 23894 alignment
• AI risk categorisation per EU AI Act risk levels + NIST AI RMF risk profile
• Ethical risk treatment per risk + mitigation + monitoring + acceptance with informed consent
• Validation of ethical outcomes + post-deployment monitoring + ethical KPI + user feedback + impact assessment
• AI safety + adversarial training + uncertainty quantification + concept drift + safe deployment + red team report

• Ethical risk register IT-focused (no harm to communities/environment)
• AI risk categorisation static (no EU AI Act conformity)
• Risk treatment paper-only (no actual mitigation)
• Validation of outcomes one-time (no ongoing monitoring)
• AI safety only at training (no operational red teaming or drift monitoring)

Governance + Culture + Integration cover organisational elements supporting IEEE 7000 implementation. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): governance + roles including: AI Ethics Officer + Ethics Review Board + Responsible AI Steering Committee + cross-functional team (engineering + legal + policy + UX + domain expert); accountability framework for AI systems per NIST AI RMF GOVERN + EU AI Act Article 17 quality management system + ISO/IEC 42001 management system; senior management commitment + Board ESG/AI oversight + reporting lines. Organisational culture and training: ethics in technology training + responsible AI training + role-based curriculum + onboarding + ongoing CPD; psychological safety + speaking-up culture + ethical concern reporting + whistleblower protection; ethical maturity assessment + cultural change man

• AI Ethics Governance + Ethics Officer + Review Board + Steering Committee + accountability framework
• Ethics training + role-based curriculum + onboarding + CPD + psychological safety + whistleblower protection
• System engineering integration + V-model + Agile + DevOps + MLOps + ethics gates + decision points
• Ethical KPI dashboard + responsible AI metrics + maturity model assessment + continuous improvement
• AI incident register + AIID reporting + regulatory compliance tracking + EU AI Act + national AI laws

• AI Ethics Officer nominal (no real authority or budget)
• Ethics training one-off (no role-based or ongoing)
• Integration siloed (ethics separate from engineering)
• KPIs financial-only (no ethical or responsible AI)
• Incident reporting absent (AI incidents not tracked or shared)

Operations + Lifecycle cover IEEE 7000 in deployed system operation. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): monitoring in operation including: ethical KPIs continuous tracking + drift monitoring (concept drift + data drift + model performance degradation + ethical performance degradation) + complaint and grievance mechanism + user feedback channels + community engagement; threshold alerts when ethical performance breaches; ongoing AI risk monitoring + emerging risk identification + horizon scanning. Data governance throughout AI lifecycle: data provenance and lineage tracking (where data came from + transformations + access) per CDISC standards + Datasheets for Datasets + Data Catalogs (Apache Atlas + DataHub + Collibra); privacy protection in AI training data + differential privacy + federated learning + synthetic data + k-a

• Ethical KPI monitoring + drift detection + threshold alerts + user feedback + community engagement
• Data provenance + lineage tracking + Datasheets for Datasets + Apache Atlas + DataHub + Collibra
• Privacy protection + differential privacy + federated learning + synthetic data + GDPR + IEEE 7002
• Safe deployment + canary + blue-green + feature flags + kill-switch + monitoring + rollback procedure
• Decommissioning procedure + secure deletion + cryptographic erasure + sunset notification + alternative provision

• Ethical KPI monitoring at deployment only (no drift detection)
• Data provenance only at acquisition (no lineage through transformations)
• Privacy protection generic (no differential privacy or federated learning for high-risk)
• Deployment without canary/kill-switch (high blast radius on failure)
• Decommissioning without secure deletion + post-decommissioning monitoring

IEEE 7000-2021 IEEE Standard Model Process for Addressing Ethical Concerns During System Design - copyrighted IEEE standard published September 2021 by IEEE Standards Association (IEEE SA). First-of-its-kind standard providing engineers + technologists + organisations with a systematic methodology to translate ethical values into system design through a Value-Based Engineering (VBE) process. Outcome of IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (founded 2016) + IEEE Ethically Aligned Design (EAD) initiative. Part of IEEE 7000 family of standards: IEEE 7000-2021 (Model Process - parent standard) + IEEE 7001-2021 (Transparency of Autonomous Systems) + IEEE 7002-2022 (Data Privacy Process) + IEEE 7003 (Algorithmic Bias Considerations) + IEEE 7004 (Child and Student Data Governance) + IEEE 7005 (Employer Data Governance) + IEEE 7006 (Personal Data AI Agent Working

• IEEE 7000-2021 applicability assessment + system scope + ethical concern relevance per project
• Value-Based Engineering process adoption + organisational charter + integration with system engineering
• IEEE 7000 family alignment matrix + 7001 Transparency + 7002 Privacy + 7003 Bias + 7010 Wellbeing applicable
• Coordination with EU AI Act + NIST AI RMF + ISO/IEC 42001 + ISO/IEC 23894 risk + OECD AI Principles
• Adoption record of OECD AI Principles + UNESCO Ethics + Council of Europe + UN HRC + Singapore Model AI Governance

• IEEE 7000 referenced but VBE process not actually adopted
• Family standards not coordinated (using 7000 without 7001/7002/7003 where applicable)
• EU AI Act + NIST AI RMF + ISO 42001 treated as separate (no integration)
• OECD/UNESCO principles signed but not operationalised
• Coordination static (no update with 2024-2025 regulatory pipeline)

Clauses 5.2 + 5.3 establish stakeholder identification and engagement. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): stakeholder identification including: direct users (purchasers + operators); indirect users (affected by system use); marginalised + vulnerable + underrepresented populations (children + elderly + persons with disabilities + ethnic minorities + LGBTQ+ + low-income + migrant + indigenous + linguistic minorities); affected non-users (those whose lives are shaped without using the system); future generations + environment + non-human stakeholders; regulators + civil society + academic + media + investors; competitors + supply chain. Stakeholder engagement methods: workshops + interviews + focus groups + ethnographic study + design thinking sprints + participatory design + co-design + value-sensitive design (VSD) methods

• Stakeholder map + direct + indirect + marginalised + non-user + future + environment + non-human
• Marginalised + vulnerable stakeholder identification + engagement + accessibility + compensation
• Engagement methods + workshops + interviews + ethnographic + design thinking + advisory boards
• Human oversight mechanism per EU AI Act Article 14 + decision review + override capability + accountability
• Co-design + VSD application + value scenarios + iterative engagement + feedback loops

• Stakeholder mapping limited to paying customers (no indirect/marginalised)
• Marginalised engagement token only (no real influence on design)
• Engagement single-event (no iteration or follow-up)
• Human oversight nominal (no real override + accountability)
• Co-design + VSD methods not used (top-down design instead)

Clause 9 establishes transparency and communication. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): transparency throughout VBE process + transparency artifacts including: system documentation (purpose + scope + values + stakeholders + risks + design rationale + verification) + decision logs + value trade-off documentation + ethical requirement traceability + risk treatment evidence + validation evidence; communication to stakeholders + users + regulators + civil society in plain language adapted to audience. Process Documentation: VBE artifacts + meeting minutes + workshop outputs + ethical review board records + change history + version control. External Review and Assurance: independent ethics review + ethics board + community advisory board + academic review + third-party audit + certification per emerging standards (ISO/IEC 4200

• VBE process documentation + decision logs + value trade-offs + ethical requirements traceability + version control
• AI system documentation per EU AI Act Annex IV + Model Cards + Datasheets + IBM FactSheets
• Algorithmic transparency + SHAP + LIME + counterfactual explanations + feature importance + decision rules
• External ethics review + ethics board + community advisory + third-party audit + certification readiness
• User notification per EU AI Act Article 50 + watermarking (C2PA + SynthID) + GDPR Article 22

• VBE process not documented (no audit trail)
• AI documentation generic (no EU AI Act Annex IV compliance)
• Algorithmic transparency post-hoc explanations only (no SHAP/LIME)
• External ethics review absent (internal-only)
• User notification missing for AI interactions (deceptive UX)

Clauses 6 + 6.1 establish ethical values elicitation and prioritisation. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): elicit values from stakeholders covering categories: human autonomy + beneficence + non-maleficence + justice + dignity + privacy + transparency + accountability + fairness + sustainability + wellbeing + cultural sensitivity + human rights. Value prioritisation: multi-criteria decision analysis + analytic hierarchy process (AHP) + value-focused thinking + trade-off analysis when values conflict; precedence rules; ethical frameworks (consequentialism + deontology + virtue ethics + care ethics + capability approach) selection per context. IEEE 7000 family integration: IEEE 7001 Transparency (transparency levels per stakeholder type + technical means + user interfaces); IEEE 7002 Data Privacy Process (privacy by design

• Value elicitation + categories + stakeholder mapping + workshops + value scenarios
• Value prioritisation + AHP + MCDA + trade-off analysis + ethical framework selection per context
• IEEE 7001 transparency levels per stakeholder + technical means + UI/UX evidence
• IEEE 7002 privacy by design + 7003 bias considerations + bias measurement (Aequitas/Fairlearn/AIF360)
• IEEE 7010 wellbeing metrics + measurement + monitoring + impact

• Value elicitation theoretical (no real stakeholder workshops)
• Prioritisation arbitrary (no MCDA or AHP methodology)
• IEEE 7001 transparency not differentiated by stakeholder
• Privacy + bias considered separately (not integrated)
• Wellbeing metrics ignored (only financial KPIs)