A survey of visual analytics for explainable artificial intelligence methods

G Alicioglu, B Sun - Computers & Graphics, 2022‏ - Elsevier
Deep learning (DL) models have achieved impressive performance in various domains such
as medicine, finance, and autonomous vehicle systems with advances in computing power …

Effective use of Likert scales in visualization evaluations: A systematic review

L South, D Saffo, O Vitek, C Dunne… - Computer Graphics …, 2022‏ - Wiley Online Library
Likert scales are often used in visualization evaluations to produce quantitative estimates of
subjective attributes, such as ease of use or aesthetic appeal. However, the methods used to …

Foundation models meet visualizations: Challenges and opportunities

W Yang, M Liu, Z Wang, S Liu - Computational Visual Media, 2024‏ - Springer
Recent studies have indicated that foundation models, such as BERT and GPT, excel at
adapting to various downstream tasks. This adaptability has made them a dominant force in …

Explaining decision-making algorithms through UI: Strategies to help non-expert stakeholders

HF Cheng, R Wang, Z Zhang, F O'connell… - Proceedings of the …, 2019‏ - dl.acm.org
Increasingly, algorithms are used to make important decisions across society. However,
these algorithms are usually poorly understood, which can reduce transparency and evoke …

Visual analytics in deep learning: An interrogative survey for the next frontiers

F Hohman, M Kahng, R Pienta… - IEEE transactions on …, 2018‏ - ieeexplore.ieee.org
Deep learning has recently seen rapid development and received significant attention due
to its state-of-the-art performance on previously-thought hard problems. However, because …

State of the art of visual analytics for explainable deep learning

B La Rosa, G Blasilli, R Bourqui, D Auber… - Computer Graphics …, 2023‏ - Wiley Online Library
The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …

A survey of visual analytics techniques for machine learning

J Yuan, C Chen, W Yang, M Liu… - Computational Visual …, 2021‏ - ieeexplore.ieee.org
Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Human factors in model interpretability: Industry practices, challenges, and needs

SR Hong, J Hullman, E Bertini - Proceedings of the ACM on Human …, 2020‏ - dl.acm.org
As the use of machine learning (ML) models in product development and data-driven
decision-making processes became pervasive in many domains, people's focus on building …

Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

F Hohman, H Park, C Robinson… - IEEE transactions on …, 2019‏ - ieeexplore.ieee.org
Deep learning is increasingly used in decision-making tasks. However, understanding how
neural networks produce final predictions remains a fundamental challenge. Existing work …

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

M Kahng, PY Andrews, A Kalro… - IEEE transactions on …, 2017‏ - ieeexplore.ieee.org
While deep learning models have achieved state-of-the-art accuracies for many prediction
tasks, understanding these models remains a challenge. Despite the recent interest in …