Part of You is in Your Bot
(2020) BUSN09 20201Department of Business Administration
- Abstract
- Purpose: With the increasing importance of unbiased conversational agents, this research investigates how debiasing strategies can be used to prevent cognitive biases from impacting conversation agents in the development process. A research gap was identified in the literature of prevention strategies for the management of cognitive biases. Therefore, existing literature on debiasing is explored and primary data is generated through interviews with experts in the field of conversational agents to gain insights for the management of cognitive biases on prevention strategies.
Methodology: A qualitative study is conducted following a systematic approach to provide objective findings. Further, literature is screened and coherently compiled... (More) - Purpose: With the increasing importance of unbiased conversational agents, this research investigates how debiasing strategies can be used to prevent cognitive biases from impacting conversation agents in the development process. A research gap was identified in the literature of prevention strategies for the management of cognitive biases. Therefore, existing literature on debiasing is explored and primary data is generated through interviews with experts in the field of conversational agents to gain insights for the management of cognitive biases on prevention strategies.
Methodology: A qualitative study is conducted following a systematic approach to provide objective findings. Further, literature is screened and coherently compiled in common underlying concepts.
Findings: Debiasing is a broad research area in which authors adopt different management approaches, either by basing strategies on specific cognitive biases or on specific causes of cognitive biases. The current literature differentiates between debiasing and prevention differently than organisations and does not focus on the application field of conversational agents. Thus, literature presents different strategies for cognitive bias management than practice. In practice, the concept of debiasing and prevention is only loosely and not uniformly defined and differentiates between debiasing and prevention on the basis of the development process. Cognitive bias management is not directed at an individual but is applied at an organisational level, wherefore cognitive bias management strategies can be both debiasing and prevention. Consequently, the literature gap is not reflected in practice.
Contribution: The research found that the theoretical gap does not pose a practical issue. Additionally, this research contributes to the current literature by providing findings from a new field of application in which strategies are presented that are potentially new. These provide proposals for future research. The literature gap of prevention, however, remains theoretical. Practical implications are further identified where education about cognitive biases should be offered to all hierarchical levels, or individuals in superordinate positions should guide individuals in operational positions on the management of cognitive bias. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9017592
- author
- Strotmann, Rica-Salome LU and Hoffmann, Helene LU
- supervisor
- organization
- course
- BUSN09 20201
- year
- 2020
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- cognitive bias, rationality, debiasing, prevention, conversational agent, artificial intelligence, biased AI
- language
- English
- id
- 9017592
- date added to LUP
- 2020-07-08 11:44:52
- date last changed
- 2020-07-08 11:44:52
@misc{9017592,
abstract = {{Purpose: With the increasing importance of unbiased conversational agents, this research investigates how debiasing strategies can be used to prevent cognitive biases from impacting conversation agents in the development process. A research gap was identified in the literature of prevention strategies for the management of cognitive biases. Therefore, existing literature on debiasing is explored and primary data is generated through interviews with experts in the field of conversational agents to gain insights for the management of cognitive biases on prevention strategies.
Methodology: A qualitative study is conducted following a systematic approach to provide objective findings. Further, literature is screened and coherently compiled in common underlying concepts.
Findings: Debiasing is a broad research area in which authors adopt different management approaches, either by basing strategies on specific cognitive biases or on specific causes of cognitive biases. The current literature differentiates between debiasing and prevention differently than organisations and does not focus on the application field of conversational agents. Thus, literature presents different strategies for cognitive bias management than practice. In practice, the concept of debiasing and prevention is only loosely and not uniformly defined and differentiates between debiasing and prevention on the basis of the development process. Cognitive bias management is not directed at an individual but is applied at an organisational level, wherefore cognitive bias management strategies can be both debiasing and prevention. Consequently, the literature gap is not reflected in practice.
Contribution: The research found that the theoretical gap does not pose a practical issue. Additionally, this research contributes to the current literature by providing findings from a new field of application in which strategies are presented that are potentially new. These provide proposals for future research. The literature gap of prevention, however, remains theoretical. Practical implications are further identified where education about cognitive biases should be offered to all hierarchical levels, or individuals in superordinate positions should guide individuals in operational positions on the management of cognitive bias.}},
author = {{Strotmann, Rica-Salome and Hoffmann, Helene}},
language = {{eng}},
note = {{Student Paper}},
title = {{Part of You is in Your Bot}},
year = {{2020}},
}