Counterfactual explanations and how to find them: literature review and benchmarking
R Guidotti - Data Mining and Knowledge Discovery, 2022 - Springer
Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …
uninterpretable classifiers. One of the most valuable types of explanation consists of …
On scientific understanding with artificial intelligence
An oracle that correctly predicts the outcome of every particle physics experiment, the
products of every possible chemical reaction or the function of every protein would …
products of every possible chemical reaction or the function of every protein would …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Evaluating the quality of machine learning explanations: A survey on methods and metrics
The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …
users, and even experts are often unable to understand the rationale behind their decisions …
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
Background Explainability is one of the most heavily debated topics when it comes to the
application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have …
application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have …
[HTML][HTML] Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
learning aimed at unboxing how AI systems' black-box choices are made. This research field …
A survey on the explainability of supervised machine learning
N Burkart, MF Huber - Journal of Artificial Intelligence Research, 2021 - jair.org
Predictions obtained by, eg, artificial neural networks have a high accuracy but humans
often perceive the models as black boxes. Insights about the decision making are mostly …
often perceive the models as black boxes. Insights about the decision making are mostly …
Dissecting recall of factual associations in auto-regressive language models
Transformer-based language models (LMs) are known to capture factual knowledge in their
parameters. While previous work looked into where factual associations are stored, only little …
parameters. While previous work looked into where factual associations are stored, only little …
[HTML][HTML] Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
A Theissler, J Pérez-Velázquez, M Kettelgerdes… - Reliability engineering & …, 2021 - Elsevier
Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …
machine learning (ML), have enabled a broad range of applications. In the automotive …
Physics-informed machine learning: case studies for weather and climate modelling
Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …