270 citations · 835 across the 84 of their papers we have counts for
6 papers · 1 filter
Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health
Arpan Dasgupta, Mizhaan Maniyar, Awadhesh Srivastava +7
Mobile health (mHealth) programs utilize automated voice messages to deliver health information, particularly targeting underserved communities, demonstrating the effectiveness of…
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…
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…
A Formal Framework to Characterize Interpretability of Procedures
Amit Dhurandhar, Vijay Iyengar, Ronny Luss +1
We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an abso…
TIP: Typifying the Interpretability of Procedures
Amit Dhurandhar, Vijay Iyengar, Ronny Luss +1
We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an abso…
Learning Causal Graphs with Small Interventions
Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis +1
We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (S…