Score breakdown

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions

paper-0113 · paper · 2019

Cynthia Rudin

Argues for inherently interpretable models where stakes are high.

Academic, score -0.1653

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent9051.00.0407380.50.020369OpenAlexmediumlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.1recorded as missing; penalized by rule, never imputed
readership_persistencepresent12.00.7857140.050.039286OpenAlexlowlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed

Broad Influence, score 0.1224

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent9051.00.0407380.20.008148OpenAlexmediumlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed
readership_persistencepresent12.00.7857140.40.314286OpenAlexlowlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.075recorded as missing; penalized by rule, never imputed

Governance Practitioner, score -0.2362

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent9051.00.0407380.250.010184OpenAlexmediumlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.15recorded as missing; penalized by rule, never imputed
readership_persistencepresent12.00.7857140.10.078571OpenAlexlowlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.175recorded as missing; penalized by rule, never imputed

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