Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search

Abstract

Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines. However, user-system interactions in audio-only channels -- as is the case for Spoken Conversational Search (SCS) -- are harder to characterize, given the lack of instruments to effectively and precisely capture interactions. Furthermore, in this era of information overload, cognitive bias can significantly impact how we seek and consume information -- especially in the context of controversial topics or multiple viewpoints. This paper draws upon insights from multiple disciplines (including information seeking, psychology, cognitive science, and wearable sensors) to provoke novel conversations in the community. To this end, we discuss future opportunities and propose a framework including multimodal instruments and methods for experimental designs and settings. We demonstrate preliminary results as an example. We also outline the challenges and offer suggestions for adopting this multimodal approach, including ethical considerations, to assist future researchers and practitioners in exploring cognitive biases in SCS.

Extended version of MobileHCI'24 LBW paper

References in corpus (7)

• Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals
• See what I'm saying? Comparing Intelligent Personal Assistant use for Native and Non-Native Language Speakers
• Characterizing Information Seeking Processes with Multiple Physiological Signals
• Towards a Better Understanding Human Reading Comprehension with Brain Signals
• Evaluating Fairness in Argument Retrieval
• Uncovering Bias in Personal Informatics
• Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot

Cited by in corpus (1)

• Towards Investigating Biases in Spoken Conversational Search