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Found 7 papers, 4 papers with code
Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection
In this paper, we provide the first benchmark study of interpretability approaches for hate speech detection.
Concept-based Explainable Artificial Intelligence: A Survey
The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models.
Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features
Existing work focuses on a few spoken language understanding (SLU) tasks, and explanations are difficult to interpret for most users.
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
Finally, we discuss how this approach can be further exploited in terms of explainability and adversarial robustness.
ITALIC: An Italian Intent Classification Dataset
Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects.
ferret: a Framework for Benchmarking Explainers on Transformers
With ferret, users can visualize and compare transformers-based models output explanations using state-of-the-art XAI methods on any free-text or existing XAI corpora.
Identifying Biased Subgroups in Ranking and Classification
Furthermore, we quantify the contribution of all attributes in the data subgroup to the divergent behavior by means of Shapley values, thus allowing the identification of the most impacting attributes.
