Year: 2020
Type: dissertation
Abstract: In this work, I examine and design sociotechnical interventions for addressing \nlimitations around data-driven accountability, particularly focusing on politically \ncontentious and systemic social issues (i.e., police accountability). While organizations \nacross sectors of society are scrambling to adopt data-driven technologies and practices, \nthere are epistemological and ethical concerns around how data use influences decisionmaking \nand actionability. My work explores how stakeholders adopt and handle the \nchallenges around being data-driven, advocating for ways HCI can mitigate such \nchallenges. \nIn this dissertation, I highlight three case studies that focus on data-driven, \nhuman-services organizations, which work with at-risk and marginalized populations. \nFirst, I examine the tools and practices of nonprofit workers and how they experience the \nmythologies associated with data use in their work. Second, I investigate how police \nofficers are adopting data-driven technologies and practices, which highlights the \nchallenges police contend with in addressing social criticisms around police \naccountability and marginalization. Finally, I conducted a case study with multiple \nstakeholders around police accountability to understand how systemic biases and \npolitically charged spaces perceive and utilize data, as well as to develop the design space \naround how alternative futures of being data-driven could support more robust and \ninclusive accountability. I examine how participants situate the concepts of power, bias, \nand truth in the data-driven practices and technologies used by and around the police. With this empirical work, I present insights that inform the HCI community at the \nintersection of data design, practice, and policies in addressing systemic social issues.
Author: Nitya Verma
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3035471824