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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