> In many important applications, AI models are trained on labeled data but when deployed in the wild, labels are not readily available (for example in medical imaging where the model is identifying a cancerous patch, "ground-truth" labels may require expert examination). A critical question is -- in the absence of…
Recent work on training neural networks to have robust attributions, to improve their trustworthiness and resilience to adversarial attacks.…
In recent work with my colleagues at CMU, we focus on bringing greater transparency to these mysterious, yet effective, machine learning techniques.…
We explain the motivation behind our 2018 NeurIPS paper, "Hunting for Discriminatory Proxies in Linear Regression Models".…
This post will delve deeper into Path Integrated Gradient Methods for Feature Attribution in Neural Networks.…
In this third post of the series on Explainability in Neural Networks, we present Axioms of Attribution, which are a set of desirable properties that any reasonable feature-attribution method should have.…
We examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.…
The wild success of Deep Neural Network (DNN) models in a variety of domains has created considerable excitement in the machine learning community. Despite this success, a deep understanding of why DNNs perform so well, and whether their performance is somehow brittle, has been lacking.…