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Why Automated Data Is Biased : = The Politics of Air Quality Data in China.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Why Automated Data Is Biased :/
Reminder of title:	The Politics of Air Quality Data in China.
Author:	Liu, Guoer.
Description:	1 online resource (171 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Political science. -
Online resource:	click for full text (PQDT)
ISBN:	9798384041863

Why Automated Data Is Biased : = The Politics of Air Quality Data in China.
Liu, Guoer.

Why Automated Data Is Biased :The Politics of Air Quality Data in China. - 1 online resource (171 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Thesis (Ph.D.)--University of Michigan, 2024.

Includes bibliographical references

Why do biases exist in government-produced data, even when the collection of that data is automated? I argue that these biases are not an outcome of agents' manipulation for personal gains, but an equilibrium that emerges from the strategic interaction between policymakers and agents, both benefiting from this arrangement. Particularly in authoritarian contexts, the accuracy of automated data can be compromised by the risk of uncovering information that undermines the regime's legitimacy. This tension between accuracy and favorability aligns the interests of political actors across and within the government hierarchy, transforming automation technology intended for objective monitoring into an instrument that obscures reality. Focusing on China's expansion of automatic ambient air quality monitoring from 2012 to 2022, I develop a game-theoretical model and present empirical evidence demonstrating how political actors strategically deploy the physical infrastructure essential for automated data collection. This analysis demonstrates that such strategic deployments compromise data accuracy even before the collection process begins. Departing from the classic principal-agent approach to biased data, this analysis emphasizes that political principals may actually implicitly endorse and propagate automated data that, while inaccurate, portrays the regime positively. This research contributes to our understanding of automated data more broadly: as technology and society progress, politically motivated data biases adapt and evolve, rather than disappear. Automatically generated data is yet another sleek end product of complex and sometimes messy upstream political processes.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798384041863Subjects--Topical Terms:

558774
Political science.
Subjects--Index Terms:

Biased government dataIndex Terms--Genre/Form:

554714
Electronic books.

Why Automated Data Is Biased : = The Politics of Air Quality Data in China.
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520 $a Why do biases exist in government-produced data, even when the collection of that data is automated? I argue that these biases are not an outcome of agents' manipulation for personal gains, but an equilibrium that emerges from the strategic interaction between policymakers and agents, both benefiting from this arrangement. Particularly in authoritarian contexts, the accuracy of automated data can be compromised by the risk of uncovering information that undermines the regime's legitimacy. This tension between accuracy and favorability aligns the interests of political actors across and within the government hierarchy, transforming automation technology intended for objective monitoring into an instrument that obscures reality. Focusing on China's expansion of automatic ambient air quality monitoring from 2012 to 2022, I develop a game-theoretical model and present empirical evidence demonstrating how political actors strategically deploy the physical infrastructure essential for automated data collection. This analysis demonstrates that such strategic deployments compromise data accuracy even before the collection process begins. Departing from the classic principal-agent approach to biased data, this analysis emphasizes that political principals may actually implicitly endorse and propagate automated data that, while inaccurate, portrays the regime positively. This research contributes to our understanding of automated data more broadly: as technology and society progress, politically motivated data biases adapt and evolve, rather than disappear. Automatically generated data is yet another sleek end product of complex and sometimes messy upstream political processes.
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653 $a Air quality
653 $a Political processes
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