Human-in-the-loop machine learning: a state of the art

E Mosqueira-Rey, E Hernández-Pereira… - Artificial Intelligence …, 2023 - Springer
Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

Explainable artificial intelligence: a comprehensive review

D Minh, HX Wang, YF Li, TN Nguyen - Artificial Intelligence Review, 2022 - Springer
Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

Masked feature prediction for self-supervised visual pre-training

C Wei, H Fan, S Xie, CY Wu, A Yuille… - Proceedings of the …, 2022 - openaccess.thecvf.com
Abstract We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training
of video models. Our approach first randomly masks out a portion of the input sequence and …

Language in a bottle: Language model guided concept bottlenecks for interpretable image classification

Y Yang, A Panagopoulou, S Zhou… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract Concept Bottleneck Models (CBM) are inherently interpretable models that factor
model decisions into human-readable concepts. They allow people to easily understand …

From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai

M Nauta, J Trienes, S Pathak, E Nguyen… - ACM Computing …, 2023 - dl.acm.org
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

From attribution maps to human-understandable explanations through concept relevance propagation

R Achtibat, M Dreyer, I Eisenbraun, S Bosse… - Nature Machine …, 2023 - nature.com
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …

[HTML][HTML] Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Z Salahuddin, HC Woodruff, A Chatterjee… - Computers in biology and …, 2022 - Elsevier
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Algorithms to estimate Shapley value feature attributions

H Chen, IC Covert, SM Lundberg, SI Lee - Nature Machine Intelligence, 2023 - nature.com
Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

[HTML][HTML] Notions of explainability and evaluation approaches for explainable artificial intelligence

G Vilone, L Longo - Information Fusion, 2021 - Elsevier
Abstract Explainable Artificial Intelligence (XAI) has experienced a significant growth over
the last few years. This is due to the widespread application of machine learning, particularly …

Representation engineering: A top-down approach to ai transparency

A Zou, L Phan, S Chen, J Campbell, P Guo… - arXiv preprint arXiv …, 2023 - arxiv.org
In this paper, we identify and characterize the emerging area of representation engineering
(RepE), an approach to enhancing the transparency of AI systems that draws on insights …