Data pricing and data asset governance in the AI Era

Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing data assets in end-to-end processes of machine l...

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Main Authors: PEI, Jian, ZHU, Feida, CONG, Zicun, XUAN, Luo, HUIWEN, Liu, MU, Xin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/6903
https://ink.library.smu.edu.sg/context/sis_research/article/7906/viewcontent/Data_Pricing_and_Data_Asset_Governance_in_the_AI_Era.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-79062022-02-07T10:49:33Z Data pricing and data asset governance in the AI Era PEI, Jian ZHU, Feida CONG, Zicun XUAN, Luo HUIWEN, Liu MU, Xin Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing data assets in end-to-end processes of machine learning and data science, that is, the pipeline where data is collected and processed, and then machine learning models are produced, requested, deployed, shared and evolved. To provide a state-of-the-art overall picture of this important and novel area and advocate the related research and development, we present a tutorial addressing two essential problems. First, in the pipeline of machine learning, how can data and machine learning models be priced properly so that contributions from various parties can be assessed and recognized in a fair manner? Second, in the collaboration among many parties in building, distributing and sharing machine learning models, how can data as assets be managed? Accordingly, the first part of our proposal surveys data and model pricing in the pipeline of machine learning, while the second part discusses data asset governance for collaborative artificial intelligence. Each part is self-contained. At the same time, the two parts echo each other and connect a series of interesting and important problems into a dynamic big picture. 2021-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6903 info:doi/10.1145/3447548.3470818 https://ink.library.smu.edu.sg/context/sis_research/article/7906/viewcontent/Data_Pricing_and_Data_Asset_Governance_in_the_AI_Era.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data asset Data pricing Data governance Consensus Blockchain Privacy Federated learning Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data asset
Data pricing
Data governance
Consensus
Blockchain
Privacy
Federated learning
Databases and Information Systems
spellingShingle Data asset
Data pricing
Data governance
Consensus
Blockchain
Privacy
Federated learning
Databases and Information Systems
PEI, Jian
ZHU, Feida
CONG, Zicun
XUAN, Luo
HUIWEN, Liu
MU, Xin
Data pricing and data asset governance in the AI Era
description Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing data assets in end-to-end processes of machine learning and data science, that is, the pipeline where data is collected and processed, and then machine learning models are produced, requested, deployed, shared and evolved. To provide a state-of-the-art overall picture of this important and novel area and advocate the related research and development, we present a tutorial addressing two essential problems. First, in the pipeline of machine learning, how can data and machine learning models be priced properly so that contributions from various parties can be assessed and recognized in a fair manner? Second, in the collaboration among many parties in building, distributing and sharing machine learning models, how can data as assets be managed? Accordingly, the first part of our proposal surveys data and model pricing in the pipeline of machine learning, while the second part discusses data asset governance for collaborative artificial intelligence. Each part is self-contained. At the same time, the two parts echo each other and connect a series of interesting and important problems into a dynamic big picture.
format text
author PEI, Jian
ZHU, Feida
CONG, Zicun
XUAN, Luo
HUIWEN, Liu
MU, Xin
author_facet PEI, Jian
ZHU, Feida
CONG, Zicun
XUAN, Luo
HUIWEN, Liu
MU, Xin
author_sort PEI, Jian
title Data pricing and data asset governance in the AI Era
title_short Data pricing and data asset governance in the AI Era
title_full Data pricing and data asset governance in the AI Era
title_fullStr Data pricing and data asset governance in the AI Era
title_full_unstemmed Data pricing and data asset governance in the AI Era
title_sort data pricing and data asset governance in the ai era
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6903
https://ink.library.smu.edu.sg/context/sis_research/article/7906/viewcontent/Data_Pricing_and_Data_Asset_Governance_in_the_AI_Era.pdf
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