Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search

TL;DR

This paper explores the detection and mitigation of cognitive biases in spoken conversational search by proposing a multimodal framework and discussing future research directions, including ethical considerations.

Contribution

It introduces a novel multimodal framework for studying cognitive biases in spoken search and discusses experimental designs and ethical challenges.

Findings

Preliminary results demonstrate feasibility of multimodal approaches.

Highlights challenges in capturing user interactions in audio-only channels.

Proposes future research directions and ethical guidelines.

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.