Towards Investigating Biases in Spoken Conversational Search

TL;DR

This paper explores biases in spoken conversational search systems, emphasizing the challenges of presenting diverse viewpoints in voice-only interfaces and proposing an experimental framework to study these biases.

Contribution

It reviews existing bias studies in web search, addresses unique challenges in voice-based systems, and proposes a research setup to investigate biases in spoken conversational search.

Findings

Identifies challenges in presenting diverse viewpoints voice-only systems.

Proposes an experimental framework for bias investigation in spoken search.

Highlights importance of balancing information and bias mitigation.

Abstract

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.