研究语音对话搜索中的偏见，提出实验框架探索观点呈现对用户态度的影响。
Towards Investigating Biases in Spoken Conversational Search
- 基于屏幕搜索研究经验，构建语音搜索偏见研究框架。
- 设计变量与数据方案，探索多视角信息呈现对用户态度的影响。
- 适合关注语音助手公平性与交互设计的研究者与工程师。
以亚马逊Alexa、谷歌助手、苹果Siri为代表的语音系统，以及OpenAI ChatGPT、微软Copilot等日益普及，服务包括视障和低识字人群在内的多样化用户，推动用户从传统搜索转向互动问答模式。然而，语音通道的线性特性使得复杂查询（如涉及争议话题的多角度信息）难以有效呈现，忽略多元观点可能加剧或引入偏见，进而影响用户态度。平衡信息负载与消除偏见对构建公平有效的语音系统至关重要。为此，本文（i）回顾屏幕端网页搜索中偏见与用户态度变化的研究方法，（ii）分析语音场景（如语音对话搜索，SCS）下相关研究的挑战，（iii）提出关键研究问题，（iv）设计包含变量、数据与测量工具的实验方案，用于探索语音环境中偏见的形成机制。
原文摘要 · Abstract (English)
Voice-based systems like Amazon Alexa, Google Assistant, and Apple Siri, along with the growing popularity of OpenAI's ChatGPT and Microsoft's Copilot, serve diverse populations, including visually impaired and low-literacy communities. This reflects a shift in user expectations from traditional search to more interactive question-answering models. However, presenting information effectively in voice-only channels remains challenging due to their linear nature. This limitation can impact the presentation of complex queries involving controversial topics with multiple perspectives. Failing to present diverse viewpoints may perpetuate or introduce biases and affect user attitudes. Balancing information load and addressing biases is crucial in designing a fair and effective voice-based system. To address this, we (i) review how biases and user attitude changes have been studied in screen-based web search, (ii) address challenges in studying these changes in voice-based settings like SCS, (iii) outline research questions, and (iv) propose an experimental setup with variables, data, and instruments to explore biases in a voice-based setting like Spoken Conversational Search.
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