Enriching Artificial Intelligence Explanations with Knowledge Fragments

Rožanec, Jože M.; Trajkova, Elena; Novalija, Inna; Zajec, Patrik; Kenda, Klemen; Fortuna, Blaž; Mladenić, Dunja

doi:10.20944/preprints202204.0073.v1

“…Data augmentation techniques at an image or embedding level have increased the models' discriminative performance [89]. Furthermore, complementing images with anomaly maps as input to supervised classification models has substantially improved discriminative capabilities [88]. The data labeling experiments showed decreased labeling accuracy by humans over time [86], which was attributed to human fatigue.…”

Section: Machine Learning and Visual Inspectionmentioning

confidence: 99%

Artificial Intelligence in Manufacturing

Chan

¹

,

Hogaboam

²

,

Cao

³

2022

Applied Innovation and Technology Management

0

View full text Add to dashboard Cite

use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

“…Data augmentation techniques at an image or embedding level have increased the models' discriminative performance [89]. Furthermore, complementing images with anomaly maps as input to supervised classification models has substantially improved discriminative capabilities [88]. The data labeling experiments showed decreased labeling accuracy by humans over time [86], which was attributed to human fatigue.…”

Section: Machine Learning and Visual Inspectionmentioning

confidence: 99%

Artificial Intelligence in Manufacturing

Harish,

Krishnaveni,

Mansurali

2021

Reinventing Manufacturing and Business Processes Through Artificial Intelligence

0

View full text Add to dashboard Cite

use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution

Mhlanga

¹

2022

View full text Add to dashboard Cite

Artificial intelligence (AI) is currently being developed by large corporations, and governments all over the world are yearning for it. AI isn’t a futuristic concept; it is already here, and it is being implemented in a range of industries. Finance, national security, health care, criminal justice, transportation, and smart cities are all examples of this. There are countless examples of AI having a substantial impact on the world and complementing human abilities. However, due to the immense societal ramifications of these technologies, AI is on the verge of disrupting a host of industries, so the technique by which AI systems are created must be better understood. The goal of the study was to look at what it meant to be human-centred, how to create human-centred AI, and what considerations should be made for human-centred AI to achieve sustainability and the SDGs. Using a systematic literature review technique, the study discovered that a human-centred AI strategy strives to create and implement AI systems in ways that benefit mankind and serve their interests. The study also found that a human-in-the-loop concept should be used to develop procedures for creating human-centred AI, as well as other initiatives, such as the promotion of AI accountability, encouraging businesses to use autonomy wisely, to motivate businesses to be aware of human and algorithmic biases, to ensure that businesses prioritize customers, and form multicultural teams to tackle AI research. The study concluded with policy recommendations for human-centred AI to help accomplish the SDGs, including expanding government AI investments, addressing data and algorithm biases, and resolving data access issues, among other things.

show abstract

Enriching Artificial Intelligence Explanations with Knowledge Fragments

Cited by 5 publications

References 18 publications

Artificial Intelligence in Manufacturing

Artificial Intelligence in Manufacturing

Artificial Intelligence in Manufacturing

Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution

Contact Info

Product

Resources

About