Generating Sparse and Valid Counterfactual Explanations for Time-Series Classification using Multi-Objective Evolutionary Optimization
TX-Gen, a novel algorithm based on evolutionary multi-objective optimization, generates a diverse set of sparse and valid counterfactual explanations for time-series classification models while maintaining proximity to the original input.
Evaluating the Effectiveness of Human and Machine Explanations for Incorrect Predictions of Large Language Models
Explanations, whether generated by humans or machines, can lead to over-reliance on incorrect AI predictions when the explanations are perceived as helpful, highlighting the dilemma of AI errors.
Leveraging Knowledge Graphs for Empirical Concept Retrieval in Explainable AI
Knowledge graphs can be leveraged to generate data-driven concepts for concept-based explainable AI, providing robust and well-aligned explanations.
Decision Predicate Graphs: A Model-Agnostic Tool for Enhancing Interpretability in Tree-Based Ensemble Models
Decision Predicate Graphs (DPG) is a model-agnostic tool that converts opaque-box tree-based ensemble models into enriched graph structures, enabling comprehensive interpretation of the model's decision-making process through the use of graph theory concepts and metrics.
SHIELD: A Regularization Technique to Enhance Explainability and Performance of Artificial Intelligence Models
SHIELD regularization aims to improve model explainability and performance by selectively concealing input features and assessing the resulting discrepancy in predictions.
Reconciling Faithfulness and Plausibility in Explainable AI: An Empirical Study Across NLP Tasks
Faithfulness and plausibility can be complementary objectives in explainable AI, as traditional perturbation-based methods like Shapley value and LIME can achieve high levels of both accuracy and user accessibility in their explanations.
