# Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search

arXiv: https://arxiv.org/abs/2405.12480 · HTML: https://mlsanity.com/paper/2405.12480/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2024.

## Built on by (most cited first, 5 of 5)

- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making](https://mlsanity.com/paper/2504.04141/) (2025)
- [Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search](https://mlsanity.com/paper/2504.02074/) (2025)
- [Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings](https://mlsanity.com/paper/2601.11049/) (2026)
- [Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agents](https://mlsanity.com/paper/2602.05597/) (2026)

## Abstract (arXiv)

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.