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The Algorithmic Link Between Auto Insurance Pricing and Unfair Discrimination.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	The Algorithmic Link Between Auto Insurance Pricing and Unfair Discrimination./
Author:	Andrews, Dorothy L.
Description:	1 online resource (449 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
Subject:	Social research. -
Online resource:	click for full text (PQDT)
ISBN:	9798379961091

The Algorithmic Link Between Auto Insurance Pricing and Unfair Discrimination.
Andrews, Dorothy L.

The Algorithmic Link Between Auto Insurance Pricing and Unfair Discrimination. - 1 online resource (449 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--Fielding Graduate University, 2023.

Includes bibliographical references

Unfair discrimination in auto insurance pricing is drawing a great deal of attention from computational journalists, regulators, and consumer groups. The attention mainly stems from the use of non-traditional insurance data engineered by third parties and "black box" machine learning algorithms that are proving to unfairly discriminate against communities of color. Communities of color often lack the privilege and resources to subvert the effects of opaque algorithms compared to White communities. A key source of unfair discrimination in auto insurance is third-party data elements, such as credit scores, that have a history of systemic discrimination against communities of color. Consumers are often unaware these data elements may be used to determine their auto insurance premiums, and their relationship to driving risk can be unintuitive. If consumers were aware, they would likely do something about it, like complain to their insurers, state insurance departments, and important others, or shop their policies with insurers who don't use third-party variables that have an unintuitive relationship to driving risk. This research was an attempt to study the kinds of complaint behaviors consumers might engage in once they are aware of the kinds of non-traditional, third-party data insurers may use to determine their auto insurance premiums. Unfortunately, it was not possible to directly observe such consumer complaint behaviors for this study. Instead, behavioral intention was used as a proxy for actual consumer complaint behavior. The theory of planned behavior provides that behavioral intention is a reliable predictor of actual behavior under certain conditions and served as the theoretical foundation for this research.

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

Mode of access: World Wide Web

ISBN: 9798379961091Subjects--Topical Terms:

1179269
Social research.
Subjects--Index Terms:

AlgorithmsIndex Terms--Genre/Form:

554714
Electronic books.

The Algorithmic Link Between Auto Insurance Pricing and Unfair Discrimination.
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500 $a Advisor: Rutledge, Pamela.
502 $a Thesis (Ph.D.)--Fielding Graduate University, 2023.
504 $a Includes bibliographical references
520 $a Unfair discrimination in auto insurance pricing is drawing a great deal of attention from computational journalists, regulators, and consumer groups. The attention mainly stems from the use of non-traditional insurance data engineered by third parties and "black box" machine learning algorithms that are proving to unfairly discriminate against communities of color. Communities of color often lack the privilege and resources to subvert the effects of opaque algorithms compared to White communities. A key source of unfair discrimination in auto insurance is third-party data elements, such as credit scores, that have a history of systemic discrimination against communities of color. Consumers are often unaware these data elements may be used to determine their auto insurance premiums, and their relationship to driving risk can be unintuitive. If consumers were aware, they would likely do something about it, like complain to their insurers, state insurance departments, and important others, or shop their policies with insurers who don't use third-party variables that have an unintuitive relationship to driving risk. This research was an attempt to study the kinds of complaint behaviors consumers might engage in once they are aware of the kinds of non-traditional, third-party data insurers may use to determine their auto insurance premiums. Unfortunately, it was not possible to directly observe such consumer complaint behaviors for this study. Instead, behavioral intention was used as a proxy for actual consumer complaint behavior. The theory of planned behavior provides that behavioral intention is a reliable predictor of actual behavior under certain conditions and served as the theoretical foundation for this research.
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653 $a Unfair discrimination
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