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Prescriptive and Descriptive Approaches to Machine-Learning Transparency
[article]
2022
arXiv
pre-print
Preserved Fulltext
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2022
CHI Conference on Human Factors in Computing Systems Extended Abstracts
unpublished
doi:10.1145/3491101.3519724 fatcat:672rrenrofazzhzshjpklu63g4
doi:10.1145/3491101.3519724 fatcat:672rrenrofazzhzshjpklu63g4
Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. ...
We further propose a preliminary approach, called Method Cards, which aims to increase the transparency and reproducibility of ML systems by providing prescriptive documentation of commonly-used ML methods ...
The Machine Learning community has recently started employing prescriptive approaches to developing ML systems. ...
arXiv:2204.13582v1
fatcat:toluuiketzcttiiuljkq6mkjfq
Contemporary Business Analytics: An Overview
2021
Data
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As business analytics has emerged as a distinct discipline with the key objective to gain insight to make informed decisions, this state-of-the art survey sheds light on recent developments in the business ...
We examine the state-of-the-art of the business analytics field by identifying and describing the four types of analytics and the three pillars of modeling. ...
.), and programming languages such as Python and packages for machine learning. ...
doi:10.3390/data6080086
fatcat:sfch7qyribdj5hhj3dktg2pkl4
Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities
[article]
2021
arXiv
pre-print
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The purpose of this short paper is to elaborate on specific characteristics of prescriptive ML and to highlight some key challenges it implies. ...
As argued in this article, prescriptive modeling comes with new technical conditions for learning and new demands regarding reliability, responsibility, and the ethics of decision making. ...
machine learning. ...
arXiv:2112.08268v1
fatcat:tulcrauy7fcv5j5j77pzhrmmy4
Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort
2020
BMJ Open
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Machine learning algorithms vary in their predictive abilities based on the dataset and the approach to analysis employed. ...
We propose to build on this by systematically applying various well-known machine learning techniques to a large longitudinal deidentified primary care database, the Optimum Patient Care Research Database ...
Acknowledgements The authors would like to thank Dominic Ng and Moiz A Shah for their contributions to the proof reading of this manuscript. We thank OPC for access to the OPCRD dataset. ...
doi:10.1136/bmjopen-2019-036099
pmid:32709646
fatcat:ujvphse52fcwrn6jqiv2t2hove
Risk Management and Analytics in Wildfire Response
2019
Current Forestry Reports
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A lack of robust descriptive analytics on wildfire incident response effectiveness is a bottleneck for developing operationally relevant and empirically credible predictive and prescriptive analytics to ...
Capitalizing on technology such as automated resource tracking and machine learning algorithms can help bridge gaps between monitoring, learning, and data-driven decision-making. ...
Machine learning techniques have some advantages over traditional approaches like generalized linear models due to their ability to handle complex problems with multiple interacting elements, and, increasingly ...
doi:10.1007/s40725-019-00101-7
fatcat:gb2fpq3kwzbf5plr5bgamjmafu
Learning analytics dashboard: a tool for providing actionable insights to learners
2022
International Journal of Educational Technology in Higher Education
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In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning ...
AbstractThis study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. ...
As the machine learning algorithms learn and induce predictive models, they move from individual and specific examples to more general descriptors of the data. ...
doi:10.1186/s41239-021-00313-7
pmid:35194560
pmcid:PMC8853217
fatcat:2aka6rxignajlg6r4wtcp2v2ie
Requirements towards optimizing analytics in industrial processes
2021
Procedia Computer Science
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We summarize key requirements for data analytics and machine learning application in industrial processes. ...
We summarize key requirements for data analytics and machine learning application in industrial processes. ...
Research on transparency and explainability of complex machine learning models is expected to facilitate acceptance and confidence. ...
doi:10.1016/j.procs.2021.03.074
fatcat:575a7543kjgtzjurl7en3lgzbu
COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions
2020
British Journal of Management
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We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study 'black swan' events such as the COVID-19-related global crisis and ...
Although scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques ...
Indeed, prescriptive analytics -in conjunction with descriptive and predictive analytics approaches such as data mining, machine learning and data visualization -is proven to substantially improve the ...
doi:10.1111/1467-8551.12441
fatcat:bvqzbpwl5zgtlkgjifznwg6pl4
Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions
2022
Big Data and Cognitive Computing
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Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. ...
From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. ...
co-evolutionary extreme learning machine. ...
doi:10.3390/bdcc6010017
fatcat:lhlrkz4a5jgrfmhe4e2gtmjeiq
Big Data Analytics Correlation Taxonomy
2019
Information
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to integrate them later in a new correlation taxonomy based on the research approaches. ...
This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries. ...
Mathematical calculation and visualisation techniques were under the descriptive and exploratory method, whereas machine learning, linear and non-linear regression, classification, data mining, text analytics ...
doi:10.3390/info11010017
fatcat:g6g7ji4arzba7ijfvcbh4jokm4
Towards an Approach Integrating Various Levels of Data Analytics to Exploit Product-Usage Information in Product Development
2019
Proceedings of the International Conference on Engineering Design
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The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. ...
and related services. ...
The authors wish to acknowledge the Commission and all the FALCON project partners for the fruitful collaboration. ...
doi:10.1017/dsi.2019.269
fatcat:4n5lyb2eunbfpg3r7l6ddsawcm
Visibility of resources and assets in the shipyard through industrial internet of things
2021
Global Journal of Computer Sciences Theory and Research
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Industrial IoT enables this data flow and monitors processes remotely and gives the ability to quickly change plans as needed. ...
Based on the findings, sensor data in the shipyard are transmitted to the cloud via connected networks. These data are analysed and combined with other information and presented to the stakeholders. ...
Artificial intelligence, machine learning, and neural network algorithms support prescriptive analytics to make specific recommendations. ...
doi:10.18844/gjcs.v11i2.5429
fatcat:26o4pqmbnree5h2pdq4d4hqjf4
Human-augmented Prescriptive Analytics with Interactive Multi-Objective Reinforcement Learning
2021
IEEE Access
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In contrast, machine learning and AI methods for decision making in stock market trading have just started to emerge. ...
The results show an agent that learns from action advice and creates a better user experience compared to an agent that learns from binary critique in terms of frustration, perceived performance, transparency ...
She has professional experience as a Software Engineer and Java Engineer. Her research interests include predictive and prescriptive analytics, machine learning and proactive computing. ...
doi:10.1109/access.2021.3096662
fatcat:r4ggrmsk6vfqjbxrouloidghnq
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research
[article]
2021
arXiv
pre-print
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We hope this field guide will help researchers more effectively design transparently interpretable models, and thus enable them to use deep learning for scientific discovery. ...
Deep learning has become popular because of its potential to achieve high accuracy in prediction tasks. ...
Instead of offering prescriptive advice, we present arguments for and against each approach. ...
arXiv:2110.08253v1
fatcat:xghw4z53fvbivkzqp3aczlbpky
A Word on Machine Ethics: A Response to Jiang et al. (2021)
[article]
2021
arXiv
pre-print
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In recent years, the fields of AI and NLP have attempted to wrangle with how learning systems that interact with humans should be constrained to behave ethically. ...
We conclude with a discussion of how machine ethics could usefully proceed, by focusing on current and near-future uses of technology, in a way that centers around transparency, democratic values, and ...
The Learning Paradigm The goal of the Delphi project is to use a supervised learning paradigm (Vapnik, 2000) to learn descriptive ethics. ...
arXiv:2111.04158v1
fatcat:ewnffpikkralvi7iupseexzotu
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