Papers by Tuukka Ruotsalo
Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attributions Explainability (2025.acl-long)
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Joakim Edin, Andreas Geert Motzfeldt, Casper L. Christensen, Tuukka Ruotsalo, Lars Maaløe, Maria Maistro
| Challenge: | Deep neural network predictions are notoriously difficult to interpret due to the difficulty in understanding their inner mechanisms. |
| Approach: | They propose to normalize AOPC to enable consistent cross-model evaluations and more meaningful interpretation of individual scores. |
| Outcome: | The proposed approach can radically change AOPC results, questioning the conclusions of earlier studies and offering a more robust framework for assessing feature attribution faithfulness. |
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records (2024.emnlp-main)
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| Challenge: | State-of-the-art explainability methods rely on human annotations, which are costly. |
| Approach: | They propose an approach to produce plausible and faithful explanations without annotations . they use adversarial robustness training to improve plausibility and AttInGrad . |
| Outcome: | The proposed method produces plausible explanations without human annotations on a medical coding task. |
As easy as PIE: understanding when pruning causes language models to disagree (2025.findings-naacl)
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| Challenge: | Language Model pruning reduces the model's efficiency by removing weights, nodes, or other parts of its architecture. |
| Approach: | They propose to prune Language Models (LMs) to produce smaller, hence more efficient models with small loss to their effectiveness. |
| Outcome: | The proposed pruning method hurts data points that matter the most when pruning . the proposed pruning technique is based on a new study of NLP datasets . |