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.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

scholarly journals Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Primož Godec ◽  
Matjaž Pančur ◽  
Nejc Ilenič ◽  
Andrej Čopar ◽  
Martin Stražar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Data Analysis ◽  
Protein Localization ◽  
Visual Programming ◽  
Developmental Competence ◽  
Image Clustering ◽  
Small Scale ◽  
Learning Approaches

Abstract Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

scholarly journals Addressing Disconnection: Automated Decision-Making, Administrative Law and Regulatory Reform

2021 ◽  
Vol 44 (3) ◽  
Author(s):  
Anna Huggins
Keyword(s):  
Decision Making ◽  
Judicial Review ◽  
Regulatory Reform ◽  
Administrative Law ◽  
Government Agencies ◽  
Mental Process ◽  
Automated Systems ◽  
Regulatory Evolution ◽  
Distinctive Features ◽  
Automated Decision Making

Automation is transforming how government agencies make decisions. This article analyses three distinctive features of automated decision-making that are difficult to reconcile with key doctrines of administrative law developed for a human-centric decision-making context. First, the complex, multi-faceted decision-making requirements arising from statutory interpretation and administrative law principles raise questions about the feasibility of designing automated systems to cohere with these expectations. Secondly, whilst the courts have emphasised a human mental process as a criterion of a valid decision, many automated decisions are made with limited or no human input. Thirdly, the new types of bias associated with opaque automated decision-making are not easily accommodated by the bias rule, or other relevant grounds of judicial review. This article, therefore, argues that doctrinal and regulatory evolution are both needed to address these disconnections and maintain the accountability and contestability of administrative decisions in the digital age.

scholarly journals A Novel Deep Learning Based Sentiment Analysis of Movie using Hybrid CNN_SVM Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 12391-12394
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Business Strategy ◽  
Performance Metrics ◽  
Classification Models ◽  
Learning Models ◽  
Meaningful Information

Data flow in web is becoming high and vast, extracting useful and meaningful information from the same is especially significant. The extracted information can be utilized for enhanced decision making. The information provided by the end-users is normally in the form of comments with respect to different products and services. Sentiment analysis is effectively carried out in these kinds of compact review to give away the people’s opinion of any products. This analyzed data will be efficient to improve the business strategy. In our work the collected online movie reviews are analyzed by using machine learning sentiment classification models like Random Forest, Naive Bayes, KNN and SVM. The work has been extended with CNN and hybrid CNN-SVM deep learning models to achieve higher performance. Comparing the workings of all the above classification models for sentiment analysis based upon various performance metrics is the main objective of the paper.

scholarly journals Algorithmic Accountability in Context. Socio-Technical Perspectives on Structural Causal Models

2021 ◽  
Vol 3 ◽  
Author(s):  
Nikolaus Poechhacker ◽  
Severin Kacianka
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Social Theory ◽  
Social System ◽  
Causal Models ◽  
The Social ◽  
Algorithmic Accountability ◽  
Research Questions ◽  
New Form ◽  
Automated Decision Making

The increasing use of automated decision making (ADM) and machine learning sparked an ongoing discussion about algorithmic accountability. Within computer science, a new form of producing accountability has been discussed recently: causality as an expression of algorithmic accountability, formalized using structural causal models (SCMs). However, causality itself is a concept that needs further exploration. Therefore, in this contribution we confront ideas of SCMs with insights from social theory, more explicitly pragmatism, and argue that formal expressions of causality must always be seen in the context of the social system in which they are applied. This results in the formulation of further research questions and directions.

scholarly journals Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

2016 ◽  
Author(s):  
Michael P. Pound ◽  
Alexandra J. Burgess ◽  
Michael H. Wilson ◽  
Jonathan A. Atkinson ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analysis ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Plant Phenotyping ◽  
Learning Approaches ◽  
Challenging Problem ◽  
Feature Identification ◽  
Art Performance

AbstractDeep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

scholarly journals Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zoe Nay ◽  
Anna Huggins ◽  
Felicity Deane
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Environmental Impact ◽  
Environmental Law ◽  
Critical Role ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Environmental Impact Assessments ◽  
Decision Making Processes ◽  
Automated Decision Making

This article critically examines the opportunities and challenges that automated decision-making (ADM) poses for environmental impact assessments (EIAs) as a crucial aspect of environmental law. It argues that while fully or partially automating discretionary EIA decisions is legally and technically problematic, there is significant potential for data-driven decision-making tools to provide superior analysis and predictions to better inform EIA processes. Discretionary decision-making is desirable for EIA decisions given the inherent complexity associated with environmental regulation and the prediction of future impacts. This article demonstrates that current ADM tools cannot adequately replicate human discretionary processes for EIAs—even if there is human oversight and review of automated outputs. Instead of fully or partially automating EIA decisions, data-driven decision-making can be more appropriately deployed to enhance data analysis and predictions to optimise EIA decision-making processes. This latter type of ADM can augment decision-making processes without displacing the critical role of human discretion in weighing the complex environmental, social and economic considerations inherent in EIA determinations.

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