Search Results for author:
Found 4 papers, 2 papers with code
Navigating Explanatory Multiverse Through Counterfactual Path Geometry
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models.
An Analysis of Physics-Informed Neural Networks
The partial differential equation is then included as a constraint in the loss function for the optimisation problem, giving the network access to knowledge of the dynamics of the physical system it is modelling.
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations
Explainable artificial intelligence techniques are developed at breakneck speed, but suitable evaluation approaches lag behind.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem.
