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Negotiating with social algorithms in the design of service personalization

Dodd, Cody (2021) Negotiating with social algorithms in the design of service personalization. PhD thesis, London School of Economics and Political Science.

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Identification Number: 10.21953/lse.00004328


Supported by three standalone yet complimentary essays, this thesis investigates the development of service personalization that has been mediated by technologies characterized as having elements of Artificial Intelligence (AI), including prediction, natural language processing, and machine learning. The aim of this work is to expand our understanding of the role emerging technologies play in affording personalization, and personalization’s relationship with systems increasingly capable of mediating experiences directly with users. Data was collected from participant observation of an AI development company over two and a half years and comprised of a detailed mapping of the technologies as well as development documents, chats, meetings, and interviews with developers and key users. We found that the implementation of deeper forms of personalization over time led to the adoption of emerging technologies like AI. In the context of a government agency, these algorithms changed the way employees are screened and selected. We also found that requests for personalization led to increasingly opaque systems where interpretation about how algorithms work emerges in place of an explanation of how they work. Building upon these findings, a framework was developed to investigate 34 discrete cases of personalization across dimensions of ease of design and ease of understanding. We found that the pursuit of deeper personalization leads to the adoption of tools that make increasingly social decisions. That is, we utilize social technologies despite their complexity because they make faster and deeper decisions about individuals from social data than can be done without them. To accomplish this, various strategies are employed to help increase user tolerance for a lack of understanding of their inner workings and to ensure they operate within bounds acceptable to users and the designers of the systems. As these systems gain increasing autonomy, issues of bias amplification, privacy, and an increasingly inexplainable logic behind decision-making remain persistent and this has implications for theory and practice.

Item Type: Thesis (PhD)
Additional Information: © 2021 Cody Dodd
Library of Congress subject classification: H Social Sciences > HC Economic History and Conditions
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Sets: Departments > Management
Supervisor: Cordella, Antonio

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