Every major technical invention resurfaces the dual-use dilemma – the ne...
With the growth of machine learning for structured data, the need for
re...
The ability to identify influential training examples enables us to debu...
Given that there are a variety of stakeholders involved in, and affected...
Necessity and sufficiency are the building blocks of all successful
expl...
Recently, a number of techniques have been proposed to explain a machine...
Explainable machine learning seeks to provide various stakeholders with
...
We present techniques for automatically inferring invariant properties o...
Deep neural networks have achieved state of the art accuracy at classify...
In this paper, we study counterfactual fairness in text classification, ...
Local explanation methods, also known as attribution methods, attribute ...
We analyze state-of-the-art deep learning models for three tasks: questi...
We study the current best model (KDG) for question answering on tabular ...
We study question-answering over semi-structured data. We introduce a ne...
Gradients have been used to quantify feature importance in machine learn...