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Found 8 papers, 2 papers with code
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?
We find that complete explanations are generally more effective when they are the same size or smaller than a contrastive explanation of the same policy, and no worse when they are larger.
Promises and Pitfalls of Black-Box Concept Learning Models
Machine learning models that incorporate concept learning as an intermediate step in their decision making process can match the performance of black-box predictive models while retaining the ability to explain outcomes in human understandable terms.
Learning Interpretable Concept-Based Models with Human Feedback
These limitations are particularly acute for high-dimensional tabular features.
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can help them decide if and how to incorporate model predictions into their decision making.
Exploring Computational User Models for Agent Policy Summarization
We introduce an imitation learning-based approach to policy summarization; we demonstrate through computational simulations that a mismatch between the model used to extract a summary and the model used to reconstruct the policy results in worse reconstruction quality; and we demonstrate through a human-subject study that people use different models to reconstruct policies in different contexts, and that matching the summary extraction model to these can improve performance.
An Evaluation of the Human-Interpretability of Explanation
Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions.
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
Human-in-the-Loop Interpretability Prior
We often desire our models to be interpretable as well as accurate.
