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Understanding and Preventing Artificial Intelligence Ethics Issues in Financial Services Organizations : = Three Studies.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding and Preventing Artificial Intelligence Ethics Issues in Financial Services Organizations :/
Reminder of title:	Three Studies.
Author:	Kelley, Stephanie.
Description:	1 online resource (141 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
Subject:	Morality. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29229418click for full text (PQDT)
ISBN:	9798841563426

Understanding and Preventing Artificial Intelligence Ethics Issues in Financial Services Organizations : = Three Studies.
Kelley, Stephanie.

Understanding and Preventing Artificial Intelligence Ethics Issues in Financial Services Organizations :Three Studies. - 1 online resource (141 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--Queen's University (Canada), 2022.

Includes bibliographical references

Reports of financial institutions proliferating discrimination, jeopardizing customer privacy, generating decisions from customer data without informed consent, and making other ethical failures in their use of artificial intelligence (AI) are increasing. As the use of AI grows, understanding and preventing the associated ethics implications is of great importance. The goal of the dissertations is to determine what the key ethical issues are arising from the use of AI in financial services organizations and study the use of AI principles (AIPs) to prevent them. AIPs are formal documents developed or selected by an organization that state normative declarations about how artificial intelligence ought to be used by its managers and employees. First, an introduction is provided that discusses the general motivation for the dissertation, a summary of the research studies included within, and the contributions of the thesis in relation to other fields studying AI ethics (Chapter 1). Second, a study is conducted to determine what the key ethical implications are of using AI in the financial services industry, drawing on literature and a semi-structured interview study of 21 employees at a large Canadian financial services institution. This understanding of AI ethics issues is presented as a Code of Conduct (one type of AIP), which has since been adopted by several Canadian financial institutions (Chapter 2). Third, an in-depth technical case study is presented that considers one ethical issue uncovered in Chapter 2: bias and discrimination. A realistically large dataset from a global fintech lender is used to simulate the impact of anti-discrimination regimes and their corresponding data management and model building guidance on gender-based discrimination. In addition, several approaches for organizations to reduce the discrimination are provided, considering the implications for model quality and firm profitability. The regulatory implications for preventing gender discrimination in non-mortgage fintech lending are then discussed (Chapter 3). Fourth, the dissertation returns to the broader organizational study of AI ethics self-regulatory initiatives and examines employee perceptions on the effective adoption of AIPs. 49 interviews were conducted with employees of 24 financial services organizations across 11 countries; the findings from which provide eleven components that could impact the effective adoption of AIPs at organizations (Chapter 4). The conclusions and practical implications of the dissertation are then summarized, followed by a discussion of limitations and future research plans (Chapter 5).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841563426Subjects--Topical Terms:

3561985
Morality.
Index Terms--Genre/Form:

542853
Electronic books.

Understanding and Preventing Artificial Intelligence Ethics Issues in Financial Services Organizations : = Three Studies.
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520 $a Reports of financial institutions proliferating discrimination, jeopardizing customer privacy, generating decisions from customer data without informed consent, and making other ethical failures in their use of artificial intelligence (AI) are increasing. As the use of AI grows, understanding and preventing the associated ethics implications is of great importance. The goal of the dissertations is to determine what the key ethical issues are arising from the use of AI in financial services organizations and study the use of AI principles (AIPs) to prevent them. AIPs are formal documents developed or selected by an organization that state normative declarations about how artificial intelligence ought to be used by its managers and employees. First, an introduction is provided that discusses the general motivation for the dissertation, a summary of the research studies included within, and the contributions of the thesis in relation to other fields studying AI ethics (Chapter 1). Second, a study is conducted to determine what the key ethical implications are of using AI in the financial services industry, drawing on literature and a semi-structured interview study of 21 employees at a large Canadian financial services institution. This understanding of AI ethics issues is presented as a Code of Conduct (one type of AIP), which has since been adopted by several Canadian financial institutions (Chapter 2). Third, an in-depth technical case study is presented that considers one ethical issue uncovered in Chapter 2: bias and discrimination. A realistically large dataset from a global fintech lender is used to simulate the impact of anti-discrimination regimes and their corresponding data management and model building guidance on gender-based discrimination. In addition, several approaches for organizations to reduce the discrimination are provided, considering the implications for model quality and firm profitability. The regulatory implications for preventing gender discrimination in non-mortgage fintech lending are then discussed (Chapter 3). Fourth, the dissertation returns to the broader organizational study of AI ethics self-regulatory initiatives and examines employee perceptions on the effective adoption of AIPs. 49 interviews were conducted with employees of 24 financial services organizations across 11 countries; the findings from which provide eleven components that could impact the effective adoption of AIPs at organizations (Chapter 4). The conclusions and practical implications of the dissertation are then summarized, followed by a discussion of limitations and future research plans (Chapter 5).
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