“Are Your Explanations Reliable?” Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack (2023.emnlp-main)
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| Challenge: | Extensive experiments on text datasets demonstrate that XAIFooler significantly outperforms all baselines by large margins in its ability to manipulate LIME’s explanations with high semantic preservability. |
| Approach: | They propose to use LIME to establish a baseline and then propose an algorithm to perturb text inputs and manipulate explanations. |
| Outcome: | The proposed algorithm outperforms baselines on text datasets and achieves high semantic preservability. |
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DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications (2023.acl-long)
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| Challenge: | Input saliency methods have become popular for explaining predictions of deep learning models, but there has been little work investigating methods for aggregating prediction-level explanations to the class level. |
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Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting (2024.emnlp-main)
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Maxime Kayser, Bayar Menzat, Cornelius Emde, Bogdan Bercean, Alex Novak, Abdalá Morgado, Bartlomiej Papiez, Susanne Gaube, Thomas Lukasiewicz, Oana-Maria Camburu
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| Challenge: | Recent studies have proposed explainable-by-design neural models providing logic explanations for their predictions, but these models favour global explanations, while local ones tend to be noisy and verbose. |
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Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020.acl-main)
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| Challenge: | a new study examines the impact of algorithmic explanations on simulatability of machine learning models . a model is simulatable when a person can predict its behavior on new inputs . |
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Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations (2022.findings-emnlp)
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| Challenge: | Existing explanations address the contrastive aspect of explanations but their extension to textual data is under-explored and there is little investigation on their vulnerabilities and limitations. |
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KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations (2023.acl-short)
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| Challenge: | eIA is an adversarial attack that generates inconsistent natural language explanations (NLEs) a model that generate In-NLE is undesirable, as it has a faulty decision-making process or is prone to inconsistencies. |
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
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Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification (2020.coling-main)
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| Challenge: | Existing methods for generating textual-based explanations are highly implausible and damage a user’s trust in the automated system. |
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