Algorithmic Explainability
The degree to which the decision-making process of an AI system can be understood and explained by humans. In regulated sectors such as finance it is a compliance requirement, and post-hoc explanation tools such as SHAP and LIME are commonly used.
Timeline (last 90 days)
Algorithmic explainability requires that algorithmic decisions be traceable and auditable, especially when model outputs directly affect public safety.
Academia calls the phenomenon in which the decision-making process of recommender systems is difficult to explain the 'algorithmic explainability' problem, and distinguishes two levels: 'transparency' (disclosing how the system works) and 'explainability' (giving human-understandable explanations for specific recommendation results).
All Facts (2)
Algorithmic explainability requires that algorithmic decisions be traceable and auditable, especially when model outputs directly affect public safety.
50%UnverifiedAcademia calls the phenomenon in which the decision-making process of recommender systems is difficult to explain the 'algorithmic explainability' problem, and distinguishes two levels: 'transparency' (disclosing how the system works) and 'explainability' (giving human-understandable explanations for specific recommendation results).
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