12 citations · 15 across the 6 of their papers we have counts for
5 papers · 1 filter
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations
Parikshit Solunke, Vitoria Guardieiro, Joao Rulff +5
With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountabili…
Gaussian Process Neural Additive Models
Wei Zhang, Brian Barr, John Paisley
Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance, where int…
The Disagreement Problem in Faithfulness Metrics
Brian Barr, Noah Fatsi, Leif Hancox-Li +3
The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work. Much of the work centers around the holy grail of providing post-…
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence
Isha Hameed, Samuel Sharpe, Daniel Barcklow +5
Explainable artificial intelligence (XAI) methods lack ground truth. In its place, method developers have relied on axioms to determine desirable properties for their explanations'…
Counterfactual Explanations via Latent Space Projection and Interpolation
Brian Barr, Matthew R. Harrington, Samuel Sharpe +1
Counterfactual explanations represent the minimal change to a data sample that alters its predicted classification, typically from an unfavorable initial class to a desired target…