Unverified60% confidenceFactExact time
Large neural networks or foundation models have a black-box problem, whereby even the developers themselves cannot fully trace the internal reasoning path behind a specific decision.
2
Sources
60%
Confidence
Long-term
Relevance
7/9/2026
First Seen
Sources
AI公共采购的透明度危机：Palantir争议给政府AI战略的警示
hackernewshackernews7/6/2026
Related Claims
UnverifiedAI不可解释性（黑箱问题）指模型做出决策的内部机制难以被人类理解和追溯，尤其在大规模神经网络中，数十亿参数之间的非线性交互无法用简单的规则描述。77% similarUnverifiedThe black-box nature of deep learning makes it difficult to explain in rule-based language why a system makes a particular decision given a specific input—that is, it lacks explainability.76% similarUnverifiedThe end-to-end neural network approach brings the challenge of insufficient interpretability, making it difficult for engineers to precisely pinpoint the cause of errors when the system makes a mistake.76% similarUnverifiedDeep neural networks are often more accurate than rule-based systems on target recognition tasks, but their decision-making processes are difficult for non-specialists to audit.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/335708API
curl https://kongchang.com/api/v1/knowledge/claims/335708MCP
get_claim(id=335708)