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Found 8 papers, 3 papers with code
Are Large Language Models Post Hoc Explainers?
To this end, several approaches have been proposed in recent literature to explain the behavior of complex predictive models in a post hoc fashion.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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On Minimizing the Impact of Dataset Shifts on Actionable Explanations
To this end, we conduct rigorous theoretical analysis to demonstrate that model curvature, weight decay parameters while training, and the magnitude of the dataset shift are key factors that determine the extent of explanation (in)stability.
Consistent Explanations in the Face of Model Indeterminacy via Ensembling
This work addresses the challenge of providing consistent explanations for predictive models in the presence of model indeterminacy, which arises due to the existence of multiple (nearly) equally well-performing models for a given dataset and task.
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods prominent in fairness, recourse and model understanding.
OpenXAI: Towards a Transparent Evaluation of Model Explanations
OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, and (ii) open-source implementations of eleven quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, in turn providing comparisons of several explanation methods across a wide variety of metrics, models, and datasets.
Global Counterfactual Explanations: Investigations, Implementations and Improvements
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding.
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates
To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a given data point where the model is uncertain, identifying a single, on-manifold change to the input such that the model becomes more certain in its prediction.
δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates
To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating Counterfactual Latent Uncertainty Explanations (CLUEs).
