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Found 14 papers, 6 papers with code
Error Discovery by Clustering Influence Embeddings
We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery.
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Missing values are a fundamental problem in data science.
Practical Policy Optimization with Personalized Experimentation
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment.
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes
Learning heterogeneous treatment effects (HTEs) is an important problem across many fields.
Interpretable Personalized Experimentation
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments.
How Interpretable and Trustworthy are GAMs?
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and interaction effects without changing the model prediction.
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations
Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms.
Axiomatic Interpretability for Multiclass Additive Models
In the first part of this paper, we generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multiclass setting, and show that this multiclass algorithm outperforms existing GAM learning algorithms and sometimes matches the performance of full complexity models such as gradient boosted trees.
Investigating Human + Machine Complementarity for Recidivism Predictions
Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of defendants in the ProPublica COMPAS data for risk of recidivism, and concluded that COMPAS predictions were no more accurate or fair than predictions made by humans.
Considerations When Learning Additive Explanations for Black-Box Models
In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations.
A Double Parametric Bootstrap Test for Topic Models
Non-negative matrix factorization (NMF) is a technique for finding latent representations of data.
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
We compare the student model trained with distillation to a second un-distilled transparent model trained on ground-truth outcomes, and use differences between the two models to gain insight into the black-box model.
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Ensembles of decision trees perform well on many problems, but are not interpretable.
