Automated decision making in highway pavement preventive maintenance based on deep learning

2022 ◽  
Vol 135 ◽  
pp. 104111
Author(s):  
Jiale Li ◽  
Guohui Yin ◽  
Xuefei Wang ◽  
Weixi Yan
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Preventive Maintenance ◽  
Automated Decision Making ◽  
Highway Pavement

Automated Decision-Making and Administrative Law

2020 ◽  
pp. 655-676
Author(s):  
Michèle Finck
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Data Analysis ◽  
Computational Intelligence ◽  
Sustainable Use ◽  
Administrative Law ◽  
Guiding Principles ◽  
Automated Decision Making

This chapter examines the uses of automated decision-making (ADM) systems in administrative settings. First, it introduces the current enthusiasm surrounding computational intelligence before a cursory overview of machine learning and deep learning is provided. The chapter thereafter examines the potential of these forms of data analysis in administrative processes. In addition, this chapter underlines that, depending on how they are used; these tools risk impacting pejoratively on established concepts of administrative law. This is illustrated through the example of the principle of transparency. To conclude, a number of guiding principles designed to ensure the sustainable use of these tools are outlined and topics for further research are suggested.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Legal and Technical Feasibility of the GDPR’s Quest for Explanation of Algorithmic Decisions: of Black Boxes, White Boxes and Fata Morganas

2020 ◽  
Vol 11 (1) ◽  
pp. 18-50 ◽  
Author(s):  
Maja BRKAN ◽  
Grégory BONNET
Keyword(s):  
Decision Making ◽  
Interdisciplinary Approach ◽  
Practical Implementation ◽  
General Data Protection Regulation ◽  
Legal Limits ◽  
Black Boxes ◽  
General Data ◽  
The Subject ◽  
The Right ◽  
Automated Decision Making

Understanding of the causes and correlations for algorithmic decisions is currently one of the major challenges of computer science, addressed under an umbrella term “explainable AI (XAI)”. Being able to explain an AI-based system may help to make algorithmic decisions more satisfying and acceptable, to better control and update AI-based systems in case of failure, to build more accurate models, and to discover new knowledge directly or indirectly. On the legal side, the question whether the General Data Protection Regulation (GDPR) provides data subjects with the right to explanation in case of automated decision-making has equally been the subject of a heated doctrinal debate. While arguing that the right to explanation in the GDPR should be a result of interpretative analysis of several GDPR provisions jointly, the authors move this debate forward by discussing the technical and legal feasibility of the explanation of algorithmic decisions. Legal limits, in particular the secrecy of algorithms, as well as technical obstacles could potentially obstruct the practical implementation of this right. By adopting an interdisciplinary approach, the authors explore not only whether it is possible to translate the EU legal requirements for an explanation into the actual machine learning decision-making, but also whether those limitations can shape the way the legal right is used in practice.

scholarly journals Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Steven A. Hicks ◽  
Jonas L. Isaksen ◽  
Vajira Thambawita ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Medical Data ◽  
Medical Doctors ◽  
Medical Tests ◽  
Deep Learning Model

AbstractDeep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction should be accompanied by an explanation that a human can understand. We present an approach called electrocardiogram gradient class activation map (ECGradCAM), which is used to generate attention maps and explain the reasoning behind deep learning-based decision-making in ECG analysis. Attention maps may be used in the clinic to aid diagnosis, discover new medical knowledge, and identify novel features and characteristics of medical tests. In this paper, we showcase how ECGradCAM attention maps can unmask how a novel deep learning model measures both amplitudes and intervals in 12-lead electrocardiograms, and we show an example of how attention maps may be used to develop novel ECG features.

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