Explainable AI (XAI): Core ideas, techniques, and solutions

R Dwivedi, D Dave, H Naik, S Singhal, R Omer… - ACM Computing …, 2023 - dl.acm.org
As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …

[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 …

[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

S Ali, T Abuhmed, S El-Sappagh, K Muhammad… - Information fusion, 2023 - Elsevier
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

“It's Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers

J Prather, BN Reeves, P Denny, BA Becker… - ACM Transactions on …, 2023 - dl.acm.org
Recent developments in deep learning have resulted in code-generation models that
produce source code from natural language and code-based prompts with high accuracy …

Deep neural networks and tabular data: A survey

V Borisov, T Leemann, K Seßler, J Haug… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Industry 4.0 vs. Industry 5.0: Co-existence, transition, or a hybrid

M Golovianko, V Terziyan, V Branytskyi… - Procedia Computer …, 2023 - Elsevier
Smart manufacturing is being shaped nowadays by two different paradigms: Industry 4.0
proclaims transition to digitalization and automation of processes while emerging Industry …

Explanations can reduce overreliance on ai systems during decision-making

H Vasconcelos, M Jörke… - Proceedings of the …, 2023 - dl.acm.org
Prior work has identified a resilient phenomenon that threatens the performance of human-
AI decision-making teams: overreliance, when people agree with an AI, even when it is …

A systematic review of explainable artificial intelligence in terms of different application domains and tasks

MR Islam, MU Ahmed, S Barua, S Begum - Applied Sciences, 2022 - mdpi.com
Artificial intelligence (AI) and machine learning (ML) have recently been radically improved
and are now being employed in almost every application domain to develop automated or …

Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review

AM Antoniadi, Y Du, Y Guendouz, L Wei, C Mazo… - Applied Sciences, 2021 - mdpi.com
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and
future potential for transforming almost all aspects of medicine. However, in many …

[图书][B] Human-centered AI

B Shneiderman - 2022 - books.google.com
The remarkable progress in algorithms for machine and deep learning have opened the
doors to new opportunities, and some dark possibilities. However, a bright future awaits …