AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
It is found that the conceptual target of a definition drives annotation divergence, while construct-preserving elaboration does not: conceptual framing significantly shifts annotations for humans and does so even more strongly for LLMs.
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 potential of lightweight transformer-based language models to enable bias detection in digital journalism with a scalable and consistent approach is demonstrated and called for further work to extend the use of machine learning for promoting fair, transparent news coverage.
The study examined whether combining claim text with speaker metadata could improve veracity classification while retaining explanations for journalistic review, and conceptualized disagreement between evidence sources as a dimension of interpretability.
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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