Search Results for author:
Found 10 papers, 3 papers with code
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome.
Artificial Neural Nets and the Representation of Human Concepts
Some go even further and believe that these concepts are stored in individual units of the network.
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research
Despite progress in the field, significant parts of current XAI research are still not on solid conceptual, ethical, or methodological grounds.
Improvement-Focused Causal Recourse (ICR)
We demonstrate that given correct causal knowledge, ICR, in contrast to existing approaches, guides towards both acceptance and improvement.
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
These descriptors are IML methods that provide insight not just into the model, but also into the properties of the phenomenon the model is designed to represent.
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions.
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Thus, an action that changes the prediction in the desired way may not lead to an improvement of the underlying target.
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
Direct importance provides causal insight into the model's mechanism, yet it fails to expose the leakage of information from associated but not directly used variables.
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
The same method that creates adversarial examples (AEs) to fool image-classifiers can be used to generate counterfactual explanations (CEs) that explain algorithmic decisions.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly.
