Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting (2024.emnlp-main)
Copied to clipboard
Maxime Kayser, Bayar Menzat, Cornelius Emde, Bogdan Bercean, Alex Novak, Abdalá Morgado, Bartlomiej Papiez, Susanne Gaube, Thomas Lukasiewicz, Oana-Maria Camburu
| Challenge: | XAI models are being used in safety-critical domains, but their use is limited due to their limited transparency and insufficient model robustness. |
| Approach: | They evaluated visual, natural language and a combination of both modalities to examine how users use them. |
| Outcome: | The proposed model is more robust and transparent than previous models. |
Similar Papers
An Annotated Corpus of Textual Explanations for Clinical Decision Support (2022.lrec-1)
Copied to clipboard
Roland Roller, Aljoscha Burchardt, Nils Feldhus, Laura Seiffe, Klemens Budde, Simon Ronicke, Bilgin Osmanodja
| Challenge: | In recent years, machine learning for clinical decision support has gained more and more attention. |
| Approach: | They propose to use XAI to provide an explanation of a model's decision making process by constructing a corpus of sentences that are annotated with different semantic layers. |
| Outcome: | The proposed models outperform physicians on very specific, narrow tasks or can help physicians to work more efficiently. |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
Copied to clipboard
| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
A Diagnostic Study of Explainability Techniques for Text Classification (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing explainability techniques that can be produced post-hoc with already trained models are lacking a definitive guide on how to choose one given a particular task and model architecture. |
| Approach: | They propose to use a list of diagnostic properties to evaluate existing explainability techniques to compare them with human annotations of salient input regions. |
| Outcome: | The proposed list compares a set of explainability techniques on downstream text classification tasks and neural network architectures. |
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020.acl-main)
Copied to clipboard
| 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 . |
| Approach: | They conduct human subject tests to isolate effect of algorithmic explanations on simulatability . they find ratings of explanations are not predictive of how helpful they are . |
| Outcome: | The results provide the first reliable estimates of how explanations influence simulatability . they show that ratings are not predictive of how helpful explanations are . |
On Evaluating Explanation Utility for Human-AI Decision Making in NLP (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a lack of evidence that explanations help people in situations they are introduced for is a problem in NLP . prior work on explainability has focused on overcoming technical challenges and used proxy evaluations. |
| Approach: | They propose to use existing metrics to evaluate the effectiveness of explanations in NLP . they argue that providing AI predictions does not cause decision makers to speed up work . |
| Outcome: | The proposed evaluations show that providing AI predictions does not cause decision makers to speed up their work without compromising performance. |
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records (2024.emnlp-main)
Copied to clipboard
| Challenge: | State-of-the-art explainability methods rely on human annotations, which are costly. |
| Approach: | They propose an approach to produce plausible and faithful explanations without annotations . they use adversarial robustness training to improve plausibility and AttInGrad . |
| Outcome: | The proposed method produces plausible explanations without human annotations on a medical coding task. |
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
| Approach: | They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values. |
| Outcome: | The proposed framework is based on the Shapley-value-based model explanations. |
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
| Approach: | They propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures. |
| Outcome: | The proposed method can better align with humans’ judgment of explanations than diagnostic or re-training measures. |
The Illusion of Competence: Evaluating the Effect of Explanations on Users’ Mental Models of Visual Question Answering Systems (2024.emnlp-main)
Copied to clipboard
Judith Sieker, Simeon Junker, Ronja Utescher, Nazia Attari, Heiko Wersing, Hendrik Buschmeier, Sina Zarrieß
| Challenge: | Using visual inputs, we hypothesize that explanations will make limited AI capabilities more transparent to users, but our results show that explanation increases users’ perceptions of the system’s competence regardless of its actual performance. |
| Approach: | They employ a visual question answer and explanation task where participants control the AI system’s limitations by manipulating visual inputs. |
| Outcome: | The proposed explanations do not increase users’ perceptions of the system’s competence regardless of its actual performance. |
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications (2023.acl-long)
Copied to clipboard
| Challenge: | Several explainability methods have been shown to be brittle in the face of adversarial perturbations of their inputs in the image and generic textual domains. |
| Approach: | They propose to adapt existing attribution robustness estimation methods to take into account domain-specific plausibility and to train networks that display robust attributions. |
| Outcome: | The proposed methods are able to characterize domain-specific plausibility and provide robust explanations on biomedical datasets. |