Domain
stringclasses 9
values | Quality Characteristic
stringlengths 5
32
| Definition
stringlengths 54
95
| Guidance
stringlengths 44
166
| Standard
stringclasses 1
value | Curator
stringclasses 1
value |
|---|---|---|---|---|---|
Functional Suitability
|
Functional completeness
|
Degree to which the set of functions covers all specified tasks and user objectives.
|
Implement rigorous requirements gathering to map all user tasks to system functions. Validate coverage through user acceptance testing (UAT). Ref: ISO/IEC/IEEE 29148.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Functional Suitability
|
Functional correctness
|
Degree to which the AI system provides the correct results with the needed degree of precision.
|
Establish ground truth datasets. Validate against accuracy metrics (F1-score, precision). Use adversarial testing. Ref: NIST AI RMF (MEASURE Map).
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Functional Suitability
|
Functional appropriateness
|
Degree to which the functions facilitate the accomplishment of specified tasks and objectives.
|
Conduct task analysis and user studies. Prioritize features based on user value. Ref: ISO 9241-210 (Human-Centered Design).
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Functional Suitability
|
Functional adaptability
|
Degree to which the AI system can be adapted for different specified tasks and environments.
|
Design systems with configurable parameters. Use feature flags and modular architecture for retraining hooks. Ref: MLOps retraining pipelines.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Performance Efficiency
|
Time behaviour
|
Degree to which response/processing times and throughput rates meet requirements.
|
Set SLOs for latency. Optimize models (quantization, pruning). Ref: Google's ML Testing Rules.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Performance Efficiency
|
Resource utilisation
|
Degree to which the amounts and types of resources used meet requirements.
|
Monitor compute/memory usage. Right-size infrastructure and auto-scaling. Ref: Green AI principles.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Performance Efficiency
|
Capacity
|
Degree to which the maximum limits of a product parameter meet requirements.
|
Load/stress testing for maximum users/transactions. Implement rate limiting. Ref: ISO 25010.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Compatibility
|
Co-existence
|
Degree to which an AI system performs efficiently while sharing a common environment.
|
Test in staging environments mirroring production. Ensure no resource monopolization. Ref: ISO 25010.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Compatibility
|
Interoperability
|
Degree to which systems can exchange information and use it.
|
Adopt standard formats (ONNX, PMML) and APIs (REST, gRPC). Schema validation. Ref: NIST AI RMF (Develop).
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
Appropriateness recognisability
|
Degree to which users can recognize whether an AI system is appropriate for their needs.
|
Provide Model Cards/Fact Sheets explaining capabilities and limitations. Ref: MIT Model Cards.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
Learnability
|
Degree to which the system enables the user to learn how to use it effectively.
|
Intuitive UI, interactive tutorials, contextual help. Ref: Nielsen Norman Group Heuristics.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
Operability
|
Degree to which an AI system is easy to operate and control.
|
Consistent UI/APIs. Effective error messages. Automate complex tasks. Ref: ISO 9241-110.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
User error protection
|
Degree to which an AI system protects users against making errors.
|
Input validation, undo functionality, constraints on invalid inputs. Ref: NIST AI RMF (Govern).
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
User interface aesthetics
|
Degree to which the UI enables pleasing and satisfying interaction.
|
Apply design systems (e.g., Material Design). Clean interfaces. Ref: ISO 9241-12x.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
Accessibility
|
Degree to which an AI system can be used by people with the widest range of capabilities.
|
Follow WCAG 2.1 (screen readers, keyboard nav, alt text). Ref: W3C WCAG.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
User controllability
|
Degree to which the user can control the AI system's behavior.
|
Settings for confidence thresholds. Allow override of AI decisions. Ref: NIST AI RMF (Human Oversight).
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Usability
|
Transparency
|
Degree to which functions and decisions are understandable to the user.
|
Implement XAI (LIME, SHAP). Document training data/algorithms. Ref: EU AI Act.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Reliability
|
Maturity
|
Degree to which an AI system meets needs for reliability under normal operation.
|
CI/CD with automated testing. Track MTBF. Canary deployments. Ref: ISO/IEC 25010.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Reliability
|
Availability
|
Degree to which an AI system is operational and accessible when required.
|
Redundancy across availability zones. Monitor uptime/SLAs. Ref: Site Reliability Engineering (SRE).
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Reliability
|
Fault tolerance
|
Degree to which an AI system operates as intended despite faults.
|
Retries with backoff, circuit breakers, fallback mechanisms. Ref: Azure AI Design Patterns.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Reliability
|
Recoverability
|
Degree to which an AI system can recover data and state after failure.
|
Automated backup/restore. Disaster Recovery (DR) plans. Track MTTR. Ref: NIST SP 800-184.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Reliability
|
Robustness
|
Degree to which an AI system functions correctly in the presence of invalid inputs or attacks.
|
Test with noisy/adversarial inputs. Adversarial training. Ref: NIST AI 100-2.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Security
|
Confidentiality
|
Degree to which data are accessible only to those authorized.
|
Encryption (rest/transit). RBAC. Anonymization/Pseudonymization. Ref: ISO/IEC 27001.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Security
|
Integrity
|
Degree to which unauthorized modification is prevented.
|
Hashing/Digital signatures for models. Immutable audit trails. Ref: NIST CSF.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Security
|
Non-repudiation
|
Degree to which actions/events can be proven to have taken place.
|
Secure logging. Digital signatures for attribution. Ref: NIST SP 800-57.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Security
|
Accountability
|
Degree to which actions can be traced uniquely to a responsible entity.
|
Clear model ownership. Audit trails of decisions. Ref: NIST AI RMF (Govern).
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Security
|
Authenticity
|
Degree to which identity of subject/resource can be proved.
|
MFA. Provenance verification of training data/models. Ref: NIST SP 800-63.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Security
|
Intervenability
|
Degree to which an AI system allows for human intervention.
|
Human-in-the-loop (HITL) processes. Kill switches/pause functions. Ref: EU AI Act.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Maintainability
|
Modularity
|
Degree to which changes to one component have minimal impact on others.
|
Loosely coupled architecture. Well-defined interfaces. Ref: Google ML Architecture.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Maintainability
|
Reusability
|
Degree to which an asset can be used in other systems.
|
Package models/features as assets. Containerization (Docker). Ref: IEEE 1517.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Maintainability
|
Analyzability
|
Degree to which one can assess the impact of an intended change.
|
Comprehensive logging/monitoring. Data lineage documentation. Ref: Observability practices.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Maintainability
|
Modifiability
|
Degree to which an AI system can be modified without defects.
|
Version control (code/data/model). Feature toggles. Ref: Martin Fowler.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Maintainability
|
Testability
|
Degree to which test criteria can be established and performed.
|
Isolated test environments. Automated regression tests. Ref: Google ML Testing Rules.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Portability
|
Installability
|
Degree to which an AI system can be successfully installed/uninstalled.
|
Docker/Helm charts. Automated deployment scripts. Ref: DevOps practices.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Portability
|
Replaceability
|
Degree to which an AI system can replace another for the same purpose.
|
Standard interfaces/protocols. Exportable data/models. Ref: ISO 25010.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Portability
|
Adaptability
|
Degree to which an AI system can be adapted for different environments.
|
Environment-agnostic design. Externalized configuration. Ref: 12-Factor App.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Effectiveness
|
Degree to which accurate and complete results are achieved.
|
Metrics aligned to user goals. Task success rate tracking. Ref: ISO 9241-11.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Efficiency
|
Degree to which results are achieved with appropriate resources.
|
Measure time-on-task. Optimize workflows. Ref: ISO 9241-11.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Usefulness
|
Degree to which the system is capable of being used to achieve specified goals.
|
Task analysis. Validate usefulness via feedback. Prioritize high-value features.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Trust
|
Degree to which the user has confidence the system will behave as intended.
|
Reliability, transparency, fairness. Clear limitations. Ref: NIST AI RMF.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Pleasure
|
Degree to which the user obtains pleasure from fulfilling personal needs.
|
User-centered design. Usability testing. Reward user actions.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Comfort
|
Degree to which the user is satisfied with physical comfort.
|
Ergonomic principles. Readable text. Assistive tech support. Ref: ISO 9241.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Transparency (Use)
|
Degree to which the user can understand functions, decisions, and outputs.
|
Natural language explanations. Actionable explanations. Ref: ISO/IEC TR 29119-11.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Economic risk mitigation
|
Degree to which the AI system mitigates potential economic risks.
|
Cost-benefit analysis. Safeguards against financial loss errors. Ref: NIST AI RMF.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Health/safety risk mitigation
|
Degree to which the AI system mitigates health and safety risks.
|
FMEA. Fail-safes. Compliance with IEC 61508.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Environment risk mitigation
|
Degree to which the AI system mitigates environment-related risks.
|
Optimize energy footprint. Renewable energy sources. Ref: Green AI.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Societal/ethical risk mitigation
|
Degree to which the AI system mitigates societal and ethical risks.
|
AI Ethics board. Bias testing. Impact assessments. Ref: EU AI Act.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Context completeness
|
Degree to which the system functions across all intended contexts.
|
Test all contexts. Diverse datasets. Monitor context drift.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
Quality in Use
|
Flexibility
|
Degree to which the system can adapt to new, unanticipated contexts.
|
Modular architecture. Transfer learning capability.
|
ISO/IEC 25059:2023
|
Prof. Hernan Huwyler
|
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