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Queens University

Blame Ascriptions Toward Autonomous Agents

Abstract

dc:description.abstract

As autonomous agents become embedded in organizational and everyday contexts, humans are increasingly delegating tasks to them. Yet when undesirable outcomes occur, people may blame the autonomous agent. This thesis investigates how blame is socially expressed, referred to as blame ascriptions, toward autonomous agents following IS delegation and undesirable outcomes . I present the results of a three-phased literature review in Chapter 2. Drawing from multiple theoretical perspectives, I develop a conceptual model and theoretical propositions in Chapter 3 that organize the process of ascribing blame toward autonomous agents into Triggering Blame, Enacting Blame, and Diagnosing Blame. Using a theoretical typology, I posit three unique blame ascriptions toward autonomous agents: Diminished Control, Ignorance, and Condemnation. Chapter 4 presents the results of a qualitative study involving 34 interviews across three domains, where I validate the proposed narratives and uncover a fourth narrative called Erosion of Skills. These narratives reveal that humans ascribe blame toward autonomous agents for superseding human control, confounding human judgment, committing moral harm, or diminishing human skills. Chapter 5 presents the results of a quantitative study, which uses an experimental vignette design to examine how the perceived agency attributes of autonomous agents influence blame ascription and IS delegation. The findings show that the perceived agency attributes of autonomy and inscrutability lead to increased blame ascription, blame ascription is negatively associated with IS delegation, and reflection may reduce the ascription of blame. This research contributes to information systems scholarship by theorizing blame ascription as a socially constructed and narratively expressed response to undesirable outcomes perceived to be caused by autonomous agents. Contrary to prevailing research that emphasizes the negative implications of blame, this thesis demonstrates that certain forms of blame ascriptions can serve a productive role in diagnosing outcomes and guiding future design and use practices. Moreover, the results of the two studies suggest the importance of context in the ascription of blame, and that reflection may play a pivotal role in the relationship between the enactment of a blame ascription and future IS delegation to autonomous agents.

Degree

thesis:*
Department dc:contributor.department
Business
Year dc:date.issued
2025

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Killoran, Jayson Andrew
Advisor dc:contributor.supervisor
  • Brohman, Kathryn

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • Attribution 4.0 International
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1974/34667
OAI identifier oai:identifier
oai:queensu.scholaris.ca:1974/34667

Chain of custody

source
Harvested from
Queens University
Base URL
qspace.library.queensu.ca/server/oai/request
Last updated
2026-07-27
Source record
OAI-PMH GetRecord
citation

Killoran, Jayson Andrew. Blame Ascriptions Toward Autonomous Agents. 2025. https://hdl.handle.net/1974/34667