Integrating Machine Learning and Human Judgment: A Study on Demand Planning in the Field

2021 ◽  
Vol 2021 (1) ◽  
pp. 16334
Author(s):  
Rebekah Inez Brau ◽  
John Aloysius ◽  
Enno Siemsen
Keyword(s):  
Machine Learning ◽  
Human Judgment ◽  
Demand Planning ◽  
On Demand

Development of a method for assessing the quality of machine translation based on ensemble methods in machine learning

2021 ◽  
Author(s):  
A.V. Kozina ◽  
Yu.S. Belov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Quality Assessment ◽  
Machine Translation ◽  
Translation System ◽  
Human Judgment ◽  
Translation Quality ◽  
Machine Translation System ◽  
Translation Systems

Automatically assessing the quality of machine translation is an important yet challenging task for machine translation research. Translation quality assessment is understood as predicting translation quality without reference to the source text. Translation quality depends on the specific machine translation system and often requires post-editing. Manual editing is a long and expensive process. Since the need to quickly determine the quality of translation increases, its automation is required. In this paper, we propose a quality assessment method based on ensemble supervised machine learning methods. The bilingual corpus WMT 2019 for the EnglishRussian language pair was used as data. The text data volume is 17089 sentences, 85% of the data was used for training, and 15% for testing the model. Linguistic functions extracted from the text in the source and target languages were used as features for training the system, since it is these characteristics that can most accurately characterize the translation in terms of quality. The following tools were used for feature extraction: a free language modeling tool based on SRILM and a Stanford POS Tagger parts of speech tagger. Before training the system, the text was preprocessed. The model was trained using three regression methods: Bagging, Extra Tree, and Random Forest. The algorithms were implemented in the Python programming language using the Scikit learn library. The parameters of the random forest method have been optimized using a grid search. The performance of the model was assessed by the mean absolute error MAE and the root mean square error RMSE, as well as by the Pearsоn coefficient, which determines the correlation with human judgment. Testing was carried out using three machine translation systems: Google and Bing neural systems, Mouses statistical machine translation systems based on phrases and based on syntax. Based on the results of the work, the method of additional trees showed itself best. In addition, for all categories of indicators under consideration, the best results are achieved using the Google machine translation system. The developed method showed good results close to human judgment. The system can be used for further research in the task of assessing the quality of translation.

The Bright but Modest Potential of Algorithms in the Courtroom

2020 ◽  
pp. 113-132
Author(s):  
Andrew Higgins ◽  
Inbar Levy ◽  
Thibaut Lienart
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Human Error ◽  
Civil Justice ◽  
Human Judgment ◽  
Judicial Decision ◽  
Judicial Decision Making ◽  
Legal Decisions ◽  
Human Skills ◽  
Civil Justice System

This chapter investigates the potential of algorithms and machine learning (ML) to improve decision-making. It considers the best roles for algorithms while maintaining important elements of human judgment. There are essential human skills in judging, but algorithms could help systematize the judicial function and thus reduce the risk of human error, inconsistency, and individual bias. Algorithmic decision-making and ML could in principle mitigate these problems since algorithms are more consistent and rely on and can synthesize more data than a human. Yet, recent proposals to use algorithms in the civil justice system are still underdeveloped and face scepticism. This chapter evaluates the risks and benefits of using algorithms in adjudication by pointing out specific elements of legal skill and expertise and identifying tasks better suited for an algorithm. While there are significant reliability and fairness limitations in using AI to make legal decisions, it is important to recognize that many of these weaknesses already exist to varying degrees in human judicial decision-making.

scholarly journals Economy-on-demand and the fairness of algorithms

2019 ◽  
Vol 10 (1) ◽  
pp. 3-16
Author(s):  
Claudia Schubert ◽  
Marc-Thorsten Hütt
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Legal Standards ◽  
On Demand ◽  
Legal Enforcement ◽  
Digital Agency

Algorithms are the key instrument for the economy-on-demand using platforms for its clients, workers and self-employed. An effective legal enforcement must not be limited to the control of the outcome of the algorithm but should also focus on the algorithm itself. This article assesses the present capacities of computer science to control and certify rule-based and data-centric (machine learning) algorithms. It discusses the legal instruments for the control of algorithms and their enforcement and institutional pre-conditions. It favours a digital agency that concentrates expertise and bureaucracy for the certification and official calibration of algorithms and promotes an international approach to the regulation of legal standards.

Big Mac: A Distributed PaaS Framework for on Demand Big Data Processing Using Machine Learning Techniques

2020 ◽  
Vol 17 (1) ◽  
pp. 92-100
Author(s):  
Balanand Jha ◽  
Kumar Abhishek ◽  
Akshay Deepak ◽  
Prakhar Shrivastav ◽  
Suraj Thakre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Techniques ◽  
Computing Power ◽  
On Demand ◽  
Learning Techniques ◽  
Ease Of Access ◽  
Start Ups

In the age of start-ups and technical research, the demand for high-end computing power and loads of space is ever increasing. Machine learning techniques have become an inseparable part of the big data analytics. Setting up one’s own infrastructure to deal with all this vastness is usually not feasible due to high expenses and lack of desired expertise. As a solution to this problem, this paper proposes a system for Big-Data Analytics and Machine Learning based on Hadoop and Spark frameworks that also supports Operating System (OS) Rental Services. Machine Learning (ML) services provide option to use both existing inbuilt popular models or create one’s own model. OS Rental services provide users with high end infrastructure on their low-end devices on rent. The entire implementation has been made open source for ease of access and facilitating extensibility.

scholarly journals Does Machine Learning Automate Moral Hazard and Error?

2017 ◽  
Vol 107 (5) ◽  
pp. 476-480 ◽  
Author(s):  
Sendhil Mullainathan ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Characteristic Feature ◽  
Health Data ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Human Judgment ◽  
Measurement Issues ◽  
Applications Of Machine Learning ◽  
A Minor

Machine learning tools are beginning to be deployed en masse in health care. While the statistical underpinnings of these techniques have been questioned with regard to causality and stability, we highlight a different concern here, relating to measurement issues. A characteristic feature of health data, unlike other applications of machine learning, is that neither y nor x is measured perfectly. Far from a minor nuance, this can undermine the power of machine learning algorithms to drive change in the health care system--and indeed, can cause them to reproduce and even magnify existing errors in human judgment.

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