Algorithmic amplification of biases on Google Search

Abstract: The evolution of information-seeking processes, driven by search engines like Google, has transformed the access to information people have. This paper investigates how individuals' preexisting attitudes influence the modern information-seeking process, specifically the results presented by Google Search. Through a comprehensive study involving surveys and information-seeking tasks focusing on the topic of abortion, the paper provides four crucial insights: 1) Individuals with opposing attitudes on abortion receive different search results. 2) Individuals express their beliefs in their choice of vocabulary used in formulating the search queries, shaping the outcome of the search. 3) Additionally, the user's search history contributes to divergent results among those with opposing attitudes. 4) Google Search engine reinforces preexisting beliefs in search results. Overall, this study provides insights into the interplay between human biases and algorithmic processes, highlighting the potential for information polarization in modern information-seeking processes.

• The paper shows that individual attitudes lead to divergent Google Search results, reinforcing users' preexisting beliefs.
• It employs surveys and structured search tasks to demonstrate that specific vocabulary choices significantly influence retrieved information.
• The study highlights that personalized search histories further tailor results, contributing to potential information polarization.

The paper "Algorithmic amplification of biases on Google Search," explores the complex interaction between human biases and algorithmic processes. This study is particularly pertinent given how search engines like Google have dramatically reshaped how people access information. The authors focus on how individual preexisting attitudes influence the search results presented by Google, using the contentious topic of abortion as a case study. The research employs a combination of surveys and structured information-seeking tasks to derive its conclusions.

Key Findings:

1. Diverse Search Results Based on Attitudes:
   • People with opposing views on abortion receive different search results. This divergence suggests that the algorithm tailors the information to align more closely with the user's preexisting beliefs.
2. Influence of Vocabulary Choices:
   • The study finds that the specific words and phrases users select when formulating their queries significantly influence the search results. This indicates that individuals' belief systems manifest in their language choices, which in turn affect the information they are exposed to.
3. Impact of Search History:
   • The user’s search history also plays a crucial role in creating divergent experiences. Personalized search results based on previous behavior can reinforce existing attitudes by consistently presenting information aligned with the user's historical search patterns.
4. Reinforcement of Preexisting Beliefs:
   • Perhaps the most critical insight is that Google Search engines reinforce preexisting beliefs. This phenomenon can lead to a kind of information polarization, where individuals are continuously exposed to viewpoints that corroborate their existing opinions, potentially limiting exposure to a broader spectrum of information.

Overall, the paper sheds light on how algorithmic processes can exacerbate human biases. It underscores the dual role of users and algorithms in shaping modern information-seeking behavior, raising important questions about information polarization and the responsibility of search engines in mitigating bias. This research is crucial for understanding how digital platforms might influence public opinion and the potential need for intervention to ensure more balanced information dissemination.