Understanding User Interaction with Biased Search Results on Controversial Topics

Core Concepts

Users' attitudes and search behaviors are influenced by confirmation bias and algorithmic biases, impacting attitude changes and search interactions.

Abstract

The study explores how cognitively biased users interact with algorithmically biased search engine result pages (SERPs) on controversial topics. Three key findings emerged: most attitude changes occur in the initial query, confirmation bias affects search behaviors, and interactions in the first query are associated with attitude changes. The study highlights the mixed effects of human biases and algorithmic biases in information retrieval tasks.

Stats

Users tend to accept information consistent with their beliefs.
Confirmation bias influences user behaviors and perceptions.
Bias can lead to biased judgments and unfair decisions.

Quotes

"Users' most attitude changes occur in the first query in a search session."
"Confirmation bias and algorithmically biased SERP presentation affect users' search interactions."

Key Insights Distilled From

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

by Ben Wang,Jiq... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17286.pdf

Deeper Inquiries

How can bias-aware user models be developed to mitigate biases effectively?

Bias-aware user models can be developed by incorporating user characteristics and search interactions to predict and understand how biases influence user behavior. These models can utilize features such as preexisting attitude strength, perceived knowledge, and openness to conflicting opinions to identify users who are more susceptible to biases. By analyzing the impact of these features on attitude changes, bias-aware user models can predict and mitigate biases effectively. Additionally, incorporating features related to search interactions, such as click-based behaviors and time-based interactions, can provide insights into how users interact with biased search results. By considering these factors, bias-aware user models can tailor interventions to mitigate biases and promote more balanced information consumption.

What are the ethical implications of algorithmic biases in user interactions?

Algorithmic biases in user interactions can have significant ethical implications, as they can lead to reinforcement of existing biases, polarization of opinions, and dissemination of misinformation. When users are exposed to biased search results that confirm their preexisting beliefs, it can create echo chambers and filter bubbles, limiting their exposure to diverse perspectives. This can further exacerbate societal biases and contribute to the spread of misinformation. Additionally, algorithmic biases can impact decision-making processes, influence user perceptions, and shape attitudes towards controversial topics. Ethical considerations arise in terms of transparency, fairness, and accountability in the design and implementation of algorithms to ensure that users are provided with unbiased and diverse information.

How can the study's findings be applied to improve search engine algorithms?

The study's findings can be applied to improve search engine algorithms by informing the design of bias-aware user models, human-centered bias mitigation techniques, and socially responsible intelligent information retrieval (IR) systems. By considering user characteristics, search interactions, and the effects of confirmation bias, search engine algorithms can be tailored to provide more balanced and unbiased search results. Incorporating features related to user attitudes, behaviors, and perceptions can help algorithms identify and mitigate biases effectively. Additionally, the study's insights on the impact of bias positions in multi-query sessions can guide the development of algorithms that promote exposure to diverse perspectives and prevent bias reinforcement. Overall, the findings can inform the design of more ethical and responsible search engine algorithms that prioritize users' well-being and provide accurate and unbiased information.