A Multi-Dimensional Evaluation of Explainability in Media Bias Detection
Detecting media bias automatically is difficult because biased framing is often subtle, yet in domains such as news analysis, accurate predictions alone are insufficient without explanations that reflect the model's underlying reasoning. We present a multi-dimensional evaluation of explainability in encoder-based media bias detection using the Bias Annotations By Experts (BABE) dataset. Specifically, we study BERT and RoBERTa as classifiers (base and large variants) along three complementary axes: predictive performance, explanation plausibility (token-level alignment with expert rationales), and mechanistic faithfulness (whether compact sets of attention heads recover predictive signal u...
| Authors | Ting Chen, Raina Zhang, Benjamin M. Ampel et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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