The state of the art in enhancing trust in machine learning models with the use of visualizations

A Chatzimparmpas, RM Martins, I Jusufi… - Computer Graphics …, 2020 - Wiley Online Library
Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

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 …

[HTML][HTML] Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence

A Holzinger, M Dehmer, F Emmert-Streib, R Cucchiara… - Information …, 2022 - Elsevier
Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …

Causalm: Causal model explanation through counterfactual language models

A Feder, N Oved, U Shalit, R Reichart - Computational Linguistics, 2021 - direct.mit.edu
Understanding predictions made by deep neural networks is notoriously difficult, but also
crucial to their dissemination. As all machine learning–based methods, they are as good as …

The impact of multiple parallel phrase suggestions on email input and composition behaviour of native and non-native english writers

D Buschek, M Zürn, M Eiband - Proceedings of the 2021 CHI Conference …, 2021 - dl.acm.org
We present an in-depth analysis of the impact of multi-word suggestion choices from a
neural language model on user behaviour regarding input and text composition in email …

Investigating gender bias in language models using causal mediation analysis

J Vig, S Gehrmann, Y Belinkov… - Advances in neural …, 2020 - proceedings.neurips.cc
Many interpretation methods for neural models in natural language processing investigate
how information is encoded inside hidden representations. However, these methods can …

A unified understanding of deep nlp models for text classification

Z Li, X Wang, W Yang, J Wu, Z Zhang… - … on Visualization and …, 2022 - ieeexplore.ieee.org
The rapid development of deep natural language processing (NLP) models for text
classification has led to an urgent need for a unified understanding of these models …

Attention flows: Analyzing and comparing attention mechanisms in language models

JF DeRose, J Wang, M Berger - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Advances in language modeling have led to the development of deep attention-based
models that are performant across a wide variety of natural language processing (NLP) …

Visual human–computer interactions for intelligent vehicles and intelligent transportation systems: The state of the art and future directions

X Wang, X Zheng, W Chen… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Research on intelligent vehicles has been popular in the past decade. To fill the gap
between automatic approaches and man-machine control systems, it is indispensable to …

Sequence-to-sequence learning with latent neural grammars

Y Kim - Advances in Neural Information Processing …, 2021 - proceedings.neurips.cc
Sequence-to-sequence learning with neural networks has become the de facto standard for
sequence modeling. This approach typically models the local distribution over the next …