TL;DR
It is suggested that predictive performance, attribution plausibility, and mechanistic faithfulness characterize different aspects of model behavior and should be evaluated separately when studying explainability in media bias detection.
Results show systematic variation across domains: technical and methodological areas such as deep learning and natural language processing exhibit gain-salient framing, while safety-critical topics such as deepfakes and facial recognition show strongly loss-salient profiles.
Media bias detection relies on definitions and examples that specify what counts as bias, yet these specifications often vary across datasets or remain implicit, even when given the same name. Such variation makes it unclear whether models trained for the same bias category learn the same construct or different phenome...
HEF-XFND is proposed, a hybrid explainable feature-fusion framework that combines sparse lexical evidence, contextual transformer representations, source-level credibility indicators, and calibrated ensemble learning that addresses three recurring limitations in fake-news research.
The Israel-Palestine conflict is one on which public opinion is greatly influenced by media bias. Detecting and understanding such bias in news reporting is necessary to promote transparency and accountability in journalism. The issue of media bias detection using advanced deep learning techniques is addressed in this...
Purpose: Automated fake news detection can support verification, but predictions are less useful when textual and contextual evidence cannot be inspected. The study examined whether combining claim text with speaker metadata could improve veracity classification while retaining explanations for journalistic review. M...
This work proposes a novel causal adjustment pipeline that iteratively selects a minimal set of SAE features via conditional independence tests, and finds that SAE representations achieve better adjustments than alternative representations in standard semi-synthetic evaluations with binary confounders, and their interp...
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