# Evaluation Metrics for Measuring Bias in Search Engine Results

arXiv: https://arxiv.org/abs/2210.10517 · HTML: https://mlsanity.com/paper/2210.10517/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2022.

## Built on by (most cited first, 12 of 12)

- [Whose Side Are You On? Investigating the Political Stance of Large Language Models](https://mlsanity.com/paper/2403.13840/) (2024)
- [Measuring and Addressing Indexical Bias in Information Retrieval](https://mlsanity.com/paper/2406.04298/) (2024)
- [Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines](https://mlsanity.com/paper/2512.09483/) (2025)
- [Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems](https://mlsanity.com/paper/2506.20209/) (2025)
- [Embracing Diversity: A Multi-Perspective Approach with Soft Labels](https://mlsanity.com/paper/2503.00489/) (2025)
- [CORG: Generating Answers from Complex, Interrelated Contexts](https://mlsanity.com/paper/2505.00023/) (2025)
- [Revealing Political Bias in LLMs through Structured Multi-Agent Debate](https://mlsanity.com/paper/2506.11825/) (2025)
- [Explaining Search Result Stances to Opinionated People](https://mlsanity.com/paper/2309.08460/) (2023)
- [Multi-Perspective Stance Detection](https://mlsanity.com/paper/2411.08752/) (2024)
- [Social and Political Framing in Search Engine Results](https://mlsanity.com/paper/2507.13325/) (2025)
- [A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback](https://mlsanity.com/paper/2310.03043/) (2023)
- [Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects](https://mlsanity.com/paper/2401.11201/) (2024)

## Abstract (arXiv)

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point. Therefore, it is important to evaluate the search engine results with respect to bias. In this work we propose novel web search bias evaluation measures which take into account the rank and relevance. We also propose a framework to evaluate web search bias using the proposed measures and test our framework on two popular search engines based on 57 controversial query topics such as abortion, medical marijuana, and gay marriage. We measure the stance bias (in support or against), as well as the ideological bias (conservative or liberal). We observe that the stance does not necessarily correlate with the ideological leaning, e.g. a positive stance on abortion indicates a liberal leaning but a positive stance on Cuba embargo indicates a conservative leaning. Our experiments show that neither of the search engines suffers from stance bias. However, both search engines suffer from ideological bias, both favouring one ideological leaning to the other, which is more significant from the perspective of polarisation in our society.