Uncertainty-aware transformers make AI decisions more transparent and trustworthy.
This paper introduces uncertainty quantification techniques for transformer models to detect dark patterns in user interfaces.
It evaluates three classification heads - Dense Neural Networks (DNNs), Bayesian Neural Networks (BNNs), and Spectral-normalized Neural Gaussian Processes (SNGPs) - comparing their performance, uncertainty estimation, and environmental impact.
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https://arxiv.org/abs/2412.05251
🤔 Original Problem:
→ Transformer models are black boxes, making it difficult to trust their predictions in critical applications like dark pattern detection, where wrong decisions can harm user autonomy.
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🔧 Solution in this Paper:
→ The paper implements uncertainty quantification at the final classification head of transformer models.
→ It compares three approaches: DNNs (baseline), BNNs (probabilistic weights), and SNGPs (distance-aware predictions).
→ Models are fine-tuned on a dark patterns dataset with 2,356 examples using different classification heads.
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💡 Key Insights:
→ SNGPs provide stable predictions with low variance (0.005) compared to BNNs
→ BNNs consume 10x more energy than DNNs for uncertainty estimation
→ Larger models like Mistral show decreased accuracy with SNGP integration
→ Model size directly correlates with carbon emissions
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