Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems

• URL: http://arxiv.org/abs/2311.06513v2
• Date: Tue, 14 Nov 2023 07:30:37 GMT
• Title: Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems
• Abstract summary: We propose a diagnosis method to attribute bias to each component of a TOD system. We conduct experiments to attribute the TOD system's bias toward three demographic axes: gender, age, and race.
• Score: 42.96878402632241
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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