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AI Risk

Transparency and explainability

"A recurring complaint among participants was a lack of knowledge about how AI systems made judgements. They emphasized the significance of making AI systems more visible and explainable so that people may have confidence in their outputs and hold them accountable for their activities. Because AI systems are typically opaque, making it difficult for users to understand the rationale behind their judgements, ethica...

AI Risk7. AI System Safety, Failures, & Limitations7.4 > Lack of transparency or interpretability2 - Post-deployment

Record summary

A quick snapshot of what this page covers.

Techniques2Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain7. AI System Safety, Failures, & LimitationsThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

"A recurring complaint among participants was a lack of knowledge about how AI systems made judgements. They emphasized the significance of making AI systems more visible and explainable so that people may have confidence in their outputs and hold them accountable for their activities. Because AI systems are typically opaque, making it difficult for users to understand the rationale behind their judgements, ethical concerns about AI, as well as issues of transparency and explainability, arise. This lack of understanding can generate suspicion and reluctance to adopt AI technology, as well as making it harder to hold AI systems accountable for their actions."

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.4 > Lack of transparency or interpretability
Entity2 - AI
Intent2 - Unintentional
Timing2 - Post-deployment
CategoryTransparency and explainability
Subcategoryn/a

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

Research source for this risk, when available.