Results for "interpretability"
Interpretability
IntermediateStudying internal mechanisms or input influence on outputs (e.g., saliency maps, SHAP, attention analysis).
Interpretability in AI is like being able to see the recipe behind a dish. It helps us understand how different ingredients (or features) affect the final outcome (or prediction) of a model. For example, if an AI predicts that someone will get a job, interpretability techniques can show which fac...
Studying internal mechanisms or input influence on outputs (e.g., saliency maps, SHAP, attention analysis).
Techniques to understand model decisions (global or local), important in high-stakes and regulated settings.
Fraction of correct predictions; can be misleading on imbalanced datasets.
Stepwise reasoning patterns that can improve multi-step tasks; often handled implicitly or summarized for safety/privacy.
Feature attribution method grounded in cooperative game theory for explaining predictions in tabular settings.
Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and experiments.
Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.
Quantifies shared information between random variables.
Required human review for high-risk decisions.
What would have happened under different conditions.
Models effects of interventions (do(X=x)).
Probability of treatment assignment given covariates.
Ensuring learned behavior matches intended objective.
Model relies on irrelevant signals.
Requirement to provide explanations.
Fabrication of cases or statutes by LLMs.
Requirement to reveal AI usage in legal decisions.
Credit models with interpretable logic.
Research ensuring AI remains safe.
Trend reversal when data is aggregated improperly.