.png){kind=icon}
Return
A Configurable Framework for Quantifying and Comparing Interpretability Across ML Models and Methods
.png){kind=icon}
OA
AI
DOI：10.3390/make8080250.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machine learning (ML) models have achieved remarkable success in areas ranging from healthcare to autonomous systems. Yet, their inherent complexity frequently obscures the reasoning behind their decisions, undermining transparency, accountability, and user trust. Compounding this issue is the absence of a universal methodology for comparing and ranking interpretability across diverse ML models and interpretability techniques. This paper addresses this gap by introducing a configurable framework of quantitative metrics to evaluate and rank interpretability. Our approach offers a structured, heuristic basis for assessing model clarity, decision logic, and accessibility, enabling practitioners to systematically compare interpretability across a wide range of algorithms and techniques. The resulting scores are intended as a practical, domain-configurable heuristic guide for comparison rather than a universal notion of interpretability.
Keywords:
explainable artificial intelligence (XAI)
interpretability metrics
interpretability ranking
Journal
M
IF:
6
Papers:
879
Citations:
1.8K
Organization
Cited Papers
"Why Should I Trust You?".png)
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
PREAI
ShareSaveShareSaveOn Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance PropagationPLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSaveShareSaveDiscovering governing equations from data by sparse identification of nonlinear dynamical systemsOAAI
OAAIShareSaveShareSaveSteering the interpretability of decision trees using lasso regression - an evolutionary perspective
PREAI
ShareSave
View more