Interpretable Machine Learning
189 papers with code • 1 benchmarks • 4 datasets
The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.
Source: Assessing the Local Interpretability of Machine Learning Models
Benchmarks
These leaderboards are used to track progress in Interpretable Machine Learning
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Interpretable Machine Learning models and implementations
CUB-200-2011
Morph Call
Controversial News Topic Datasets
SkinCon
Most implemented papers
Interpretable Explanations of Black Boxes by Meaningful Perturbation
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions.
Interpretable machine learning: definitions, methods, and applications
Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv. org/abs/1806. 05337
Neural Additive Models: Interpretable Machine Learning with Neural Nets
They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine
In this article, we introduce a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the key learning elements of a TM.
ProtoAttend: Attention-Based Prototypical Learning
google-research/google-research
•
•
We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes.
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains.
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains.
Explaining a black-box using Deep Variational Information Bottleneck Approach
SeojinBang/VIBI
•
•
Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
suinleelab/attributionpriors
•
•
ICLR 2020
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions have certain desirable properties -- most frequently, that particular features are important or unimportant.
Explaining Groups of Points in Low-Dimensional Representations
A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent.
