69 citations · 93 across the 11 of their papers we have counts for
4 papers · 1 filter
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Dennis Wei, Rahul Nair, Amit Dhurandhar +3
Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interp…
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
James McCarthy, Rahul Nair, Elizabeth Daly +2
Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context. Understanding the decisions and…
User Driven Model Adjustment via Boolean Rule Explanations
Elizabeth M. Daly, Massimiliano Mattetti, Öznur Alkan +1
AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landsca…
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Öznur Alkan, Dennis Wei, Massimiliano Mattetti +3
Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in s…