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Found 10 papers, 4 papers with code
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and generalize intricate patterns hidden in massive datasets.
Gaussian Process Neural Additive Models
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 interpretable and explainable models are required.
The Disagreement Problem in Faithfulness Metrics
The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work.
Calibrate: Interactive Analysis of Probabilistic Model Output
Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection.
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence
By perturbing the input variables in rank order of importance, the goal is to assess the sensitivity of the model's performance.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets
We also contribute SuRE, a visual analytics (VA) system that integrates HSR and interactive surrogate rule visualizations.
Topological Representations of Local Explanations
Furthermore, due to the stochastic nature of some explainability methods, it is possible for different runs of a method to produce contradictory explanations for a given observation.
Counterfactual Explanations via Latent Space Projection and Interpolation
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 class.
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations
In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance.
Towards Ground Truth Explainability on Tabular Data
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.
