Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)

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Investigation and Empirical Analysis of Personal Information Collected by Digital Business Platforms

From the Perspective of Big Data Discriminatory Pricing (BDDP)

Authors
Huali Chen1, Fei Wang2, *
1School of Law, Guangzhou Xinhua University, Tianhe District, Guangzhou, China
2School of Management, Guangzhou Xinhua University, Tianhe District, Guangzhou, China
*Corresponding author. Email: 1318382963@qq.com
Corresponding Author
Fei Wang
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-064-0_44How to use a DOI?
Keywords
Algorithmic Consumer; Information Collection; Big Data Discriminatory Pricing (BDDP); Informed Consent; Price Discrimination
Abstract

The rapid development of big data and artificial intelligence is changing the traditional economic model. By collecting a wealth of personal information from the consumer manipulated by algorithms and then using algorithmic technology to accurately profile them, digital business platforms provide greater scope for their pricing activities, thus giving rise to the phenomenon of “big data discriminatory pricing (BDDP)”. The research revealed that the consumer manipulated by algorithms are not fully aware of exactly of what personal details are being collected from them and are not enjoying the full rights of informed consent, resulting in a lack of consent to price discrimination. The correlation analysis of the data suggests that digital business platforms should disclose the personal details collected from individual the consumer manipulated by algorithms to safeguard their rights of informed consent, so as to build a platform of trust between the two parties and adopt reasonable price discrimination under the premise of legal compliance.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
978-94-6463-064-0
ISSN
2589-4900
DOI
10.2991/978-94-6463-064-0_44How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Huali Chen
AU  - Fei Wang
PY  - 2022
DA  - 2022/12/27
TI  - Investigation and Empirical Analysis of Personal Information Collected by Digital Business Platforms
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
PB  - Atlantis Press
SP  - 410
EP  - 420
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-064-0_44
DO  - 10.2991/978-94-6463-064-0_44
ID  - Chen2022
ER  -