Towards Bias-Free AI-Supported Decision-Making: Exploring Organisational and Technology Characteristics for Algorithmic Bias Mitigation
(2023) INFM10 20231Department 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:
http://lup.lub.lu.se/student-papers/record/9120214
- author
- Lohyna, David LU and Pico Oristrell, Anna LU
- supervisor
-
- Odd Steen LU
- organization
- course
- INFM10 20231
- year
- 2023
- 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}}, }