# Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics

arXiv: https://arxiv.org/abs/2403.17286 · HTML: https://mlsanity.com/paper/2403.17286/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2024.

## Built on by (most cited first, 6 of 6)

- [AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment](https://mlsanity.com/paper/2409.16022/) (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)
- [The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges](https://mlsanity.com/paper/2411.15396/) (2024)
- [Not All Transparency Is Equal: Source Presentation Effects on Attention, Interaction, and Persuasion in Conversational Search](https://mlsanity.com/paper/2512.12207/) (2025)
- [From SERPs to Sound: How Search Engine Result Pages and AI-generated Podcasts Interact to Influence User Attitudes on Controversial Topics](https://mlsanity.com/paper/2601.11282/) (2026)

## Abstract (arXiv)

When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude changes of users searching online, our study examined how cognitively biased users interact with algorithmically biased search engine result pages (SERPs). We designed three-query search sessions on debated topics under various bias conditions. We recruited 1,321 crowdsourcing participants and explored their attitude changes, search interactions, and the effects of confirmation bias. Three key findings emerged: 1) most attitude changes occur in the initial query of a search session; 2) Confirmation bias and result presentation on SERPs affect the number and depth of clicks in the current query and perceived familiarity with clicked results in subsequent queries; 3) The bias position also affects attitude changes of users with lower perceived openness to conflicting opinions. Our study goes beyond traditional simulation-based evaluation settings and simulated rational users, sheds light on the mixed effects of human biases and algorithmic biases in information retrieval tasks on debated topics, and can inform the design of bias-aware user models, human-centered bias mitigation techniques, and socially responsible intelligent IR systems.