Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Towards Bias-Free AI-Supported Decision-Making: Exploring Organisational and Technology Characteristics for Algorithmic Bias Mitigation

Lohyna, David LU and Pico Oristrell, Anna LU (2023) INFM10 20231
Department of Informatics
Abstract
With the growing adoption of data analytics and powerful algorithms in organisational decision-making, the ethical concerns surrounding algorithmic bias have become increasingly apparent. This research aims to explore the extent to which organisational characteristics (e.g., laws, policies, norms, and standards) and technology characteristics (e.g., transparency, user control,
and auditability) address algorithmic bias, examining their potential impact, and associated challenges and limitations. In order to achieve this aim, the study involves a comprehensive literature review to gather existing knowledge on the subject, followed by semi-structured interviews to collect insights and perspectives from relevant participants. Although the... (More)
With the growing adoption of data analytics and powerful algorithms in organisational decision-making, the ethical concerns surrounding algorithmic bias have become increasingly apparent. This research aims to explore the extent to which organisational characteristics (e.g., laws, policies, norms, and standards) and technology characteristics (e.g., transparency, user control,
and auditability) address algorithmic bias, examining their potential impact, and associated challenges and limitations. In order to achieve this aim, the study involves a comprehensive literature review to gather existing knowledge on the subject, followed by semi-structured interviews to collect insights and perspectives from relevant participants. Although the study findings have revealed that organisational and technology characteristics are crucial in
addressing algorithmic bias, particular challenges and limitations have been shown to hinder the effectiveness of its mitigation. This study calls for further research on strategies for mitigating algorithmic bias and improvement upon the challenges and limitations identified. (Less)
Please use this url to cite or link to this publication:
author
Lohyna, David LU and Pico Oristrell, Anna LU
supervisor
organization
course
INFM10 20231
year
type
H1 - Master's Degree (One Year)
subject
keywords
Algorithmic Bias, AI-Supported Decision-Making, Organisational Characteristics, Technology Characteristics
language
English
id
9120214
date added to LUP
2023-06-14 10:21:54
date last changed
2023-06-14 10:21:54
@misc{9120214,
  abstract     = {{With the growing adoption of data analytics and powerful algorithms in organisational decision-making, the ethical concerns surrounding algorithmic bias have become increasingly apparent. This research aims to explore the extent to which organisational characteristics (e.g., laws, policies, norms, and standards) and technology characteristics (e.g., transparency, user control,
and auditability) address algorithmic bias, examining their potential impact, and associated challenges and limitations. In order to achieve this aim, the study involves a comprehensive literature review to gather existing knowledge on the subject, followed by semi-structured interviews to collect insights and perspectives from relevant participants. Although the study findings have revealed that organisational and technology characteristics are crucial in
addressing algorithmic bias, particular challenges and limitations have been shown to hinder the effectiveness of its mitigation. This study calls for further research on strategies for mitigating algorithmic bias and improvement upon the challenges and limitations identified.}},
  author       = {{Lohyna, David and Pico Oristrell, Anna}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Towards Bias-Free AI-Supported Decision-Making: Exploring Organisational and Technology Characteristics for Algorithmic Bias Mitigation}},
  year         = {{2023}},
}