AI System Impact
ISO/IEC 42005Based on ISO/IEC 42005: 8 impact dimensions × 25 sub-dimensions, linked to 208 risk scenarios. Select a dimension to explore.
5 sub-dimensions01Accountability33→Evaluates the state and responsibility of individuals or entities being accountable for the actions and decisions of the AI system. Because the automated nature of AI systems may alter existing accountability frameworks, organizations must assess whether corresponding legal and ethical responsibility assignment frameworks have been established. Specific evaluations include monitoring and preventing significant negative impacts , human oversight and override mechanisms , verification of intended purpose and suitability , and incident log recording and traceability.2 sub-dimensions02Transparency12→Ensures that activities, decisions, capabilities, and limitations regarding the AI system are disclosed to all relevant stakeholders in a comprehensive, clear, and understandable manner. Specific evaluations include mandatory disclosure of AI interaction and generated content (to prevent deepfake deception) , communication of system capabilities and limitations (to avoid automation bias) , and transparency of training data sources and basic model architecture. 4 sub-dimensions03Fairness & Non-discrimination19→Evaluates whether the AI system outputs unfairly favor or discriminate against specific individuals, groups, or parts of society, avoiding harm to protected and vulnerable populations caused by algorithmic bias, unfair treatment, or cultural barriers.3 sub-dimensions04Privacy15→Ensures that the collection and use of personally identifiable information (PII) are properly controlled, kept confidential, and not abused during the development, input, or output processes of the AI system, preventing unauthorized disclosure, excessive surveillance, and personal data breaches.3 sub-dimensions05Reliability34→Evaluates whether the AI system can perform correctly under specified requirements and demonstrate consistent expected behavior and results, specifically preventing performance degradation caused by data drift, excessive error rates, and unstable outputs.3 sub-dimensions06(Safety and Security )78→Ensures that the AI system, under defined conditions, does not cause harm or physical threat to human life, health, well-being, property, or the environment, while guarding against malicious threats such as adversarial attacks, data poisoning, and model theft.3 sub-dimensions07Explainability3→Evaluates the human ability to understand "how the AI system reaches a specific output," which directly affects stakeholders' trust in the system, ensuring decision logic is transparent and providing recourse mechanisms for affected parties to challenge and appeal outputs.2 sub-dimensions08Environmental Impact14→Evaluates the ecological and resource impacts of the AI system on the natural environment throughout its entire lifecycle, from development and training to deployment, including direct computational resource consumption, greenhouse gas carbon emissions, and cooling water consumption, as well as indirect behavioral impacts brought by commercial applications.