In this paper we propose a novel method that provides contrastive explanations justifying the
classification of an input by a black box classifier such as a deep neural network. Given an …

It is still not possible to predict whether a given molecule will have a perceived odor or what
olfactory percept it will produce. We therefore organized the crowd-sourced DREAM …

Rapid advances in artificial intelligence (AI) and availability of biological, medical, and
healthcare data have enabled the development of a wide variety of models. Significant success …

The standard risk minimization paradigm of machine learning is brittle when operating in
environments whose test distributions are different from the training distribution due to spurious …

As artificial intelligence and machine learning algorithms make further inroads into society,
calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At …

Artificial intelligence systems are being increasingly deployed due to their potential to increase
the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many …

As machine learning algorithms make inroads into our lives and society, calls are increasing
from multiple stakeholders for these algorithms to explain their outputs. Moreover, these …

Prototypical examples that best summarize and compactly represent an underlying complex
data distribution, communicate meaningful insights to humans in domains where simple …

Several strands of research have aimed to bridge the gap between artificial intelligence (AI)
and human decision-makers in AI-assisted decision-making, where humans are the …

Recently, a method [7] was proposed to generate contrastive explanations for differentiable
models such as deep neural networks, where one has complete access to the model. In this …