Concept
Explainable artificial intelligence
Related publications (117)
Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model
Yi Zhang, Wenlong Liao, Zhe Yang
Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...
Piscataway2024Ethological computational psychiatry: Challenges and opportunities
Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions ...
Current Biology Ltd2024Towards Trustworthy Deep Learning for Image Reconstruction
The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...
EPFL2024InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts
Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell
Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...
2024It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons
Matthias Grossglauser, Aswin Suresh, Chi Hsuan Wu
We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...
2024The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
Vinitra Swamy, Jibril Albachir Frej
Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...
2023Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design
Vinitra Swamy, Mirko Marras, Sijia Du
Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle th ...
2023Exploiting Explanations to Detect Misclassifications of Deep Learning Models in Power Grid Visual Inspection
In the context of automatic visual inspection of infrastructures by drones, Deep Learning (DL) models are used to automatically process images for fault diagnostics. While explainable Artificial Intelligence (AI) algorithms can provide explanations to asse ...
Research Publishing2023