Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems
Recent works have shown considerable improvements in task-oriented dialogue (TOD) systems by utilizing pretrained large language models (LLMs) in an end-to-end manner. However, the biased behavior of each component in a TOD system and the error propagation issue in the end-to-end framework can lead...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
11.11.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Recent works have shown considerable improvements in task-oriented dialogue
(TOD) systems by utilizing pretrained large language models (LLMs) in an
end-to-end manner. However, the biased behavior of each component in a TOD
system and the error propagation issue in the end-to-end framework can lead to
seriously biased TOD responses. Existing works of fairness only focus on the
total bias of a system. In this paper, we propose a diagnosis method to
attribute bias to each component of a TOD system. With the proposed attribution
method, we can gain a deeper understanding of the sources of bias.
Additionally, researchers can mitigate biased model behavior at a more granular
level. We conduct experiments to attribute the TOD system's bias toward three
demographic axes: gender, age, and race. Experimental results show that the
bias of a TOD system usually comes from the response generation model. |
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DOI: | 10.48550/arxiv.2311.06513 |