explainable machine learning

Disentangling Interactions and Dependencies in Feature Attributions AISTATS 2025 Analysis of Pathological Speech – Pitfalls along the Way Interspeech 2024 Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining ACL 2024 LR-XFL: Logical Reasoning-Based Explainable Federated Learning AAAI 2024 On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods AAAI 2024 Robust Counterfactual Explanations in Machine Learning: A Survey IJCAI 2024 Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution NeurIPS 2024 An Explainable Forecasting System for Humanitarian Needs Assessment AAAI 2023 ASAP: Attention-Based State Space Abstraction for Policy Summarization ACML 2023 Fairness and Explainability: Bridging the Gap towards Fair Model Explanations AAAI 2023 Faith-Shap: The Faithful Shapley Interaction Index JMLR 2023 From Shapley Values to Generalized Additive Models and back AISTATS 2023 On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results JMLR 2023 Amortized Generation of Sequential Algorithmic Recourses for Black-Box Models AAAI 2022 Explaining Preferences with Shapley Values NeurIPS 2022 Robust Counterfactual Explanations for Tree-Based Ensembles ICML 2022 The Good, the Bad, and the Explainer: A Tool for Contrastive Explanations of Text Classifiers IJCAI 2022 dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python JMLR 2021 Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification ACL 2021 HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection AAAI 2021 “Something Something Hota Hai!” An Explainable Approach towards Sentiment Analysis on Indian Code-Mixed Data EMNLP 2021 Detecting and Analysing Spontaneous Oral Cancer Speech in the Wild Interspeech 2020 Impact of Politically Biased Data on Hate Speech Classification EMNLP 2020 Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals AAAI 2019 Towards Explainable NLP: A Generative Explanation Framework for Text Classification ACL 2019