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Found 6 papers, 3 papers with code
Models of human preference for learning reward functions
One promising method for alignment is to learn the reward function from human-generated preferences between pairs of trajectory segments.
The Irrationality of Neural Rationale Models
We call for more rigorous and comprehensive evaluations of these models to ensure desired properties of interpretability are indeed achieved.
Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development
Practitioners from diverse occupations and backgrounds are increasingly using machine learning (ML) methods.
Do Feature Attribution Methods Correctly Attribute Features?
Feature attribution methods are exceedingly popular in interpretable machine learning.
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx.
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach
In this paper, we consider the problem of exploring the prediction level sets of a classifier using probabilistic programming.
