270 citations · 669 across the 40 of their papers we have counts for
5 papers · 1 filter
TED: Teaching AI to Explain its Decisions
Michael Hind, Dennis Wei, Murray Campbell +5
Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However,…
Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection
Karthik S. Gurumoorthy, Amit Dhurandhar
In this paper, we show that if the optimization function is restricted-strongly-convex (RSC) and restricted-smooth (RSM) -- a rich subclass of weakly submodular functions -- then a…
Improving Simple Models with Confidence Profiles
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss +1
In this paper, we propose a new method called ProfWeight for transferring information from a pre-trained deep neural network that has a high test accuracy to a simpler interpretabl…
Teaching Meaningful Explanations
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy +5
The adoption of machine learning in high-stakes applications such as healthcare and law has lagged in part because predictions are not accompanied by explanations comprehensible to…
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss +4
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. Gi…