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Tag: Search Bias

Challenging the status quo in search engine ranking algorithms

Challenging the status quo in search engine ranking algorithms

How can we bring more fairness to search result ranking? This was the question tackled by our FATE (Fairness Accountability Transparency Ethics) group in the 2020 Text REtrieval Conference’s (TREC) Fairness Ranking Track. In the context of searching for academic papers, the assigned goal of the track was the goal was to develop an algorithm that provides fair exposure to different groups of authors while ensuring that the papers are relevant to the search queries. 

The Approach

To achieve that goal, the group decided to use “gender” and “country” as key attributes because they were general enough to be applied to all author groups. From there, the group created an  fairness-aware algorithm that was used to run two specific tasks: 

  1. An information retrieval task where the goal was to return a ranked list of papers to serve as the candidate papers for re-ranking
  2. Re-ranking task where the goal was to rank the candidate papers based on the relevance to a given query, while accounting for fair author group exposure

To evaluate the relevance of the academic papers, the group relied on BM25, which is an algorithm frequently used by search engines.

The Findings

By randomly shuffling the academic papers, the result was high levels of fairness if only the gender of the authors was considered. In contrast, if only the country of the authors was  considered, fairness was relatively lower. With the proposed algorithm, data can be re-ranked based on an arbitrary number of group definitions. However, to fully provide fair and relevant results, more attributes need to be explored. 

Why is fairness in search rankings important?

We use search engines everyday to find out information and answers for almost everything in our lives. And the ranking of the search results determine what kind of content we are likely to consume. This poses a risk because ranking algorithms often leave out the underrepresented groups, whether it’s a small business, or a research lab that is not established yet. At the same time, the results tend to only show information we like to see or agree with, which could lack diversity and contribute to bias. 

Interested in learning more? Check out the full research paper here: 

FATE Research Group: From Why to What and How

FATE Research Group: From Why to What and How

When the public started getting access to the internet, search engines became common in daily usage. Services such as Yahoo, AltaVista, and Google were used to satisfy people’s curiosity. Although it was not comfortable using search engines because users had to go back and forth between all the search engines, it seemed like magic that users could get so much information in a very short time. At that time, users started using search engines without any previous training. Before search engines became popular, the public generally found information in libraries by reading the library catalog or asking a librarian for help. In contrast, typing a few keywords is enough to find answers on the internet. Not only that, but search engines have been continually developing their own algorithms and giving us great features, such as knowledge bases that enhance their search engine results with information gathered from various sources.

Soon enough, Google became the first choice for many people due to its accuracy and high-quality results. As a result, other search engines got dominated by Google. However, while Google results are high-quality, those results are biased. According to a recent study, the top web search results from search engines are typically shown to be biased. Some of the results on the first page are made to be there just to capture users’ attention. At the same time, users tend to click mostly on results that appear on the first page. The study gives an example about a normal topic: coffee and health. In the first 20 results, there are 17 results about the health benefits, while only 3 results mentioned the harms.

This problem led our team at the InfoSeeking Lab to start a new project known as Fairness, Accountability, Transparency, Ethics (FATE). In this project, we have been exploring ways to balance the inherent bias found in search engines and fulfill a sense of fair representation while effectively maintaining a high degree of utility.

We started this experiment with one big goal, which is to improve fairness. For that, we designed a new system that shows two sets of results, both of which are very similar to Google’s dashboard. (as illustrated by picture below).  We have collected 100 queries and top 100 results per query from Google in general topics such as sports, food, travel, etc. One of these sets is obtained from Google. The other one is generated through an algorithm that reduces bias. The system has 20 rounds. The system gives a user 30 seconds on each round to choose the set they prefer.

For this experiment, we asked around 300 participants to participate. The goal is to see if participants can notice a difference between our algorithms and Google. The early results show that participants preferred our algorithms more than Google. However, we will discuss more in detail as soon as we finish the analysis process. Furthermore, we are in the process of writing a technical paper and an academic article.

Also, we have designed a game that looks very similar to our system. This game tests the ability to notice bad results. It gives you a score and some advice. In this game, users can also challenge their friends or members of their families. To try this game, click here

For many years, the InfoSeeking Lab has worked on issues related to information retrieval, information behavior, data science, social media, and human-computer interaction. Visit the InfoSeeking Lab website to know more about our projects

For more information about the experiment visit FATE project website

Ruoyuan Gao successfully defends her dissertation

Ruoyuan Gao successfully defends her dissertation

Ruoyuan Gao, Ph.D. student

Our Ph.D. student, Ruoyuan Gao, has successfully defended her dissertation titled “Toward a Fairer Information Retrieval System”. The committee included  Chirag Shah (University of Washington, Chair), Yongfeng Zhang (Rutgers University), Gerard de Melo (Rutgers University), and Fernando Diaz (Microsoft).

Ruoyuan investigated the existing bias presented in search engine results to understand the relationship between relevance and fairness in the results. She developed frameworks that could effectively identify the fairness and relevance in a data set. She also proposed an evaluation metric for the ranking results that encoded fairness, diversity, novelty, and relevance. With this matric, she developed algorithms that optimized both diversity fairness and relevance for search results.


With the increasing popularity and social influence of information retrieval (IR) systems, various studies have raised concerns on the presence of bias in IR and the social responsibilities of IR systems. Techniques for addressing these issues can be classified into pre-processing, in-processing and post-processing. Pre-processing reduces bias in the data that is fed into the machine learning models. In-processing encodes the fairness constraints as a part of the objective function or learning process. Post-processing operates as a top layer over the trained model to reduce the presentation bias exposed to users. This dissertation explored ways to bring the pre-processing and post-processing approaches, together with the fairness-aware evaluation metrics, into a unified frame- work as an attempt to break the vicious cycle of bias.

We first investigated the existing bias presented in search engine results. Specifically, we focused on the top-k fairness ranking in terms of statistical parity fairness and disparate impact fairness definitions. With Google search and a general purpose text cluster as a lens, we explored several topical diversity fairness ranking strategies to understand the relationship between relevance and fairness in search results. Our experimental results show that different fairness ranking strategies result in distinct utility scores and may perform differently with distinct datasets. Second, to further investigate the relationship of data and fairness algorithms, we developed a statistical framework that was able to facilitate various analysis and decision making. Our framework could effectively and efficiently estimate the domain of data and solution space. We derived theoretical expressions to identify the fairness and relevance bounds for data of different distributions, and applied them to both synthetic datasets and real world datasets. We presented a series of use cases to demonstrate how our framework was applied to associate data and provide insights to fairness optimization problems. Third, we proposed an evaluation metric for the ranking results that encoded fairness, diversity, novelty and relevance. This metric offered a new perspective of evaluating fairness-aware ranking results. Based on this metric, we developed effective ranking algorithms that optimized for diversity fairness and relevance at the same time. Our experiments showed that our algorithms were able to capture multiple aspects of the ranking and optimize the proposed fairness-aware metric.