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IA-07 Explainability
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.
🎯 BenefitsProvides feature attribution and transparent decision logic to help stakeholders understand output rationale, while establishing effective appeal and recourse mechanisms for affected individuals.
⚠️ Harms / RisksBlack-box models cannot provide specific judgment rationale; when unfair or erroneous decisions occur, affected individuals cannot understand the reasons and lack effective appeal and recourse mechanisms.
Adequacy of Information for Decision-Making
DefinitionEvaluates whether sufficient information is available for users to make informed decisions based on AI outputs.
🎯 BenefitsDeploys explainable AI (XAI) techniques, ensuring that automated decisions from black-box models provide concrete, comprehensible, and sufficient feature-attribution rationale.
⚠️ Harms / RisksWhen customers request explanations for adverse decisions such as loan or insurance denials, the system can output only an abstract score, unable to provide the specific features and reasoning behind the judgment.
Representative ScenariosS0057 Deep Model Decisions Resistant to Explanation
Explainability Across Lifecycle Stages
DefinitionEvaluates the impact of explainability across the design and development, verification and validation, and deployment stages.
🎯 BenefitsMaintains traceability of system logic across the full lifecycle from design and validation to operational monitoring, recording inputs, parameters, and intermediate reasoning.
⚠️ Harms / RisksThe black-box nature of deep learning makes it impossible to reconstruct the reasoning path when incidents occur, and explanation tools themselves produce misleading or unstable results, obstructing root-cause analysis.
Representative ScenariosS0018 Economic Disruption of Creative Industries
Trust and the Ability to Challenge
DefinitionEvaluates the impact of explainability on users' trust in the AI system and their ability to challenge AI outputs.
🎯 BenefitsProvides transparent decision explanations to affected individuals and auditors, safeguarding the right of those affected to understand the reasons, file appeals, and challenge outcomes.
⚠️ Harms / RisksThose affected by automated determinations cannot obtain case-specific explanations, and the organization provides no substantive appeal or human review channel, depriving individuals of their rights.
Representative ScenariosS0059 Opaque Decisions Obstructing Accountability