Unverified50% confidenceOpinionExact time
Reversibility is a key dimension in system-level AI evaluation that directly determines the magnitude of the consequences of failure, referring to the extent to which an automated decision or action can be undone, corrected, or compensated before causing irreparable harm.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedReproducibility is an important technical indicator of rigor in AI safety evaluations; if an evaluation cannot be repeated by other independent teams using the same methods to obtain consistent results, the credibility of its conclusions is significantly undermined.77% similarUnverified"Falling back to human review when AI fails" is referred to in engineering practice as graceful degradation, and is a pragmatic choice for striking a balance between user experience and system reliability.75% similarPartially VerifiedIn high-risk scenarios, systems should be designed to fail conservatively—when AI is uncertain, it is better to hand off to human processing than to risk automatic execution.73% similarUnverified可控退出在AI安全领域对应「可中断性」（interruptibility）或「可纠正性」（corrigibility）概念72% similarUnverifiedUnpredictable behavior in AI safety research typically refers to models producing outputs outside the training distribution that developers did not anticipate, encompassing phenomena such as jailbreaking, goal drift, and emergent capabilities.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1011310API
curl https://kongchang.com/api/v1/knowledge/claims/1011310MCP
get_claim(id=1011310)