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Found 13 papers, 10 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.
"KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding
Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional neural architectures, yet their application to speech processing remains under explored.
A Synthetic Benchmark to Explore Limitations of Localized Drift Detections
Concept drift is a common phenomenon in data streams where the statistical properties of the target variable change over time.
Detecting Interpretable Subgroup Drifts
The ability to detect and adapt to changes in data distributions is crucial to maintain the accuracy and reliability of machine learning models.
KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
This paper analyzes the integration of KAN layers into the U-Net architecture (U-KAN) to segment crop fields using Sentinel-2 and Sentinel-1 satellite images and provides an analysis of the performance and explainability of these networks.
A Contrastive Learning Approach to Mitigate Bias in Speech Models
Speech models may be affected by performance imbalance in different population subgroups, raising concerns about fair treatment across these groups.
A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data
Kolmogorov-Arnold Networks (KANs) have very recently been introduced into the world of machine learning, quickly capturing the attention of the entire community.
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.
