124 citations · 344 across the 42 of their papers we have counts for
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Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates
Dan Ley, Umang Bhatt, Adrian Weller
To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a…
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement
Umang Bhatt, Isabel Chien, Muhammad Bilal Zafar +1
As the complexity of machine learning (ML) models increases, resulting in a lack of prediction explainability, several methods have been developed to explain a model's behavior in…
Do Concept Bottleneck Models Learn as Intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt +3
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets. Such models aim to incorporate pre-specified, high-level concepts into the learning pr…
δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates
Dan Ley, Umang Bhatt, Adrian Weller
To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating Counterfactual Latent Uncertainty Explanations (CLUEs). However, fo…
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Ana Lucic, Madhulika Srikumar, Umang Bhatt +4
Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders ha…