38 Algorithms: Invisibly Shaping Our Research and Ideas

Overview

We just learned about identifying appropriate keywords and subject headings to find the most relevant sources. But when you use any search engine, there’s another factor that determines what results you see first: algorithms.

Let’s say you’re being asked to take a stand on an issue. How do you find the different points of view on that issue? How do you decide what views you agree with? Your views on any given issue might be shaped by your education, by trusted people in your life, and by your own personal experiences. But how much of it is also shaped or reinforced by what you see online? That’s where algorithms come into play.

Algorithms are step-by-step instructions that computers follow to complete tasks, solve problems, and make automated decisions. They use data to make predictions about people, including their preferences, attributes, and behaviors. Algorithms power nearly everything we see online, including search engines, social media, video games, online dating, and smartphone apps. They are used to shape and filter content on the platforms many of us interact with daily, such as Google, YouTube, Instagram, Netflix, Facebook, Amazon, Twitter, and Spotify. For example, algorithms determine which websites you see first in your Google search results, which posts you see on Facebook, and which videos YouTube “recommends” and autoplays for you.

You may use Google every day, but do you really know how its search result-ranking algorithm works? Here’s a quick video [view time is 5:15] in which Google explains why some results appear at the top of your search results, while others don’t:

As companies, governments, and other organizations continue to collect and analyze massive amounts of our data, the use of algorithms has become pervasive. In fact, many are referring to this period as the “Age of Algorithms” or the “Algorithm Era,” and researchers are considering the significant impacts that these tools may have—both positive and negative.

There is no doubt that algorithms can be useful and help to improve our lives. For example, it certainly saves a lot of time and frustration to be able to pull up a map on our phones and instantly determine the fastest way to reach our destination. However, as technology and social media scholar danah boyd has noted, “the same technology can be used to empower people…or harm them. It all depends on who is using the information to what ends” (qtd. in Rainie and Anderson). Eni Mustafaraj, Assistant Professor of Computer Science at Wellesley College, similarly notes that “if we want more people in the world to have access to the total human knowledge accessible on the Internet, we need algorithms. However, what we need to object against are the values driving the companies that own these algorithms” (qtd. in Head et al. 42).

Concerns

What happens when algorithms are used to predict when college students are “cheating” on a test, or to predict who should be hired for a job or who should get a loan, or to decide the type of information we see in our social media newsfeeds, or to calculate credit scores, or even to predict criminal behavior and determine prison sentences? Are Google search results really an unbiased presentation of the best available information on a research question? How do algorithms impact our perception of a research topic, or of our own realities?

Algorithmic Bias

Because we often assume that algorithms are neutral and objective, they can inaccurately project greater authority than human expertise. Thus, the pervasiveness of algorithms—and their incredible potential to influence our society, politics, institutions, and behavior—has been a source of growing concern.

Algorithmic bias is one of those key concerns. This occurs when algorithms reflect the implicit values of the humans involved in their creation or use, systematically “replicating or even amplifying human biases, particularly those affecting protected groups” (Lee et al.). In search engines, for example, algorithmic bias can create search results that reflect racist, sexist, or other social biases, despite the presumed neutrality of the data. Here are just a few examples of algorithmic bias (Lee et al.):

• An algorithm used by judges to predict whether defendants should be imprisoned or released on bail was found to be biased against African-Americans.
• Amazon had to discontinue using a recruiting algorithm after discovering gender bias: The algorithm was penalizing any resume that contained the word “women’s” in the text, because the data was based on resumes historically submitted to Amazon, which were predominantly from white males.
• Princeton University researchers analyzed algorithms and found that they picked up on existing racial and gender biases: European names were perceived as more pleasant than those of African-Americans, and the words “woman” and “girl” were more likely to be associated with the arts instead of science and math.
• Numerous articles [New Tab] have examined the role that YouTube’s recommendation algorithm might play in radicalizing viewers.

Now, consider that the large language models powering generative AI chatbots like ChatGPT were trained on this data.

Challenging the Algorithms of Oppression

In the video below [view time is 20:34], digital media scholar Dr. Safiya Umoja Noble and computer scientist Dr. Latanya Sweeney discuss their findings about algorithmic bias in Google search results and ads, and how it particularly affects women of color.

Content warning: The following video includes discussions of racism, sexism, and racial violence. It also shows sexualized images  found in search results.

Video 38.2. Search Engine Breakdown: Are Algorithms Racist and Sexist? | Full Film by NOVA PBS