What is recorded here?
Adds annotation-weighted input-gradient penalties to prediction loss so models avoid selected irrelevant features, and uses gradient constraints to search for alternative decision boundaries.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Local derivative penalties depend on annotation quality and differentiability; they do not guarantee global causal correctness or arbitrary distribution-shift robustness. General ML method only, not the clinical motivation or applications.
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People and systems
Andrew Slavin Ross; Michael C. Hughes; Finale Doshi-Velez
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Right for the Right Reasons: masked input-gradient regularization
Adds annotation-weighted input-gradient penalties to prediction loss so models avoid selected irrelevant features, and uses gradient constraints to search for alternative decision boundaries.
Scientific scope
Local derivative penalties depend on annotation quality and differentiability; they do not guarantee global causal correctness or arbitrary distribution-shift robustness. General ML method only, not the clinical motivation or applications.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Andrew Slavin Ross; Michael C. Hughes; Finale Doshi-Velez
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.