Algorithms and Bias 

   image source: tctmd.com

                      

    This week, I decided to learn more about algorithms, how they work, and how they affect what people view online. Before digging into some resources, I knew that algorithms could be biased but didn't realize to what extent. The phrase "it's just math" came up a lot in the resources I read and listened to, when in reality algorithms are not "just math." Algorithms are created by human beings, which means that technology, specifically algorithms, is not untouched by human bias.The resources I interacted with this week explained bias in algorithms, but also revealed some of the benefits of using algorithms. 

    Algorithms are an important topic to understand because as educators, we need to teach our students to access reliable, accurate information. We also need students to understand the concept of bias and how to fact-check and question the information they consume. This relates to my current learning space because I often have students use the Internet to complete research. Although we do discuss credibility in sources, I have never talked to students about bias in algorithms. Some of my students will get information from the first website that appears after a search instead of browsing several resources and then deciding which to use in an essay or project. 

    One of the most surprising discoveries from this week was how much faith people put into algorithms in regards to accuracy and unbiased information. According to "Why Search Engines Aren't Unbiased With Safiya Noble", many people believe that "...the mere presence of complex math assures accuracy and that technology has an authority we should respect, if not obey" (Conover). Adam Conover goes on to give examples of algorithms giving wrong information to users. One such story is about a marathon runner who became upset that the course wasn't the correct length when in fact the FitBit they were wearing measured the wrong distance. The FitBit is not always accurate when counting the number of steps or the exact distance of a run, but people are ready to believe that it is always accurate. Conover goes on to describe people putting their trust in technologies such as GPS and self-driving cars without questioning how well they work or how they may be flawed. Humans created these technologies and humans make errors and have biases. When it comes to the topic of search engines, humans created the algorithm that can lead to bias in search results. Some companies even pay search engines like Google to get their content to show up first. Therefore, algorithms are no longer just "math", but influenced by humans and their interests. 

    Steven Bellovin's article "Yes, 'algorithms' can be biased. Here's why.", also discusses this idea. Bellovin mentions instances when algorithms became discriminatory against women or used racist terms to describe people. As also mentioned in the podcast, I learned about the idea of GIGO (Garbage In, Garbage Out). Essentially, this idea is that the quality of the output depends on the quality of input. If people input biased or inaccurate information, that is what the system will output. I've never heard of this phrase before, but it is a great way to describe the phenomenon of "fake news" and unreliable sources. 

image source: researchgate.net

    Although algorithms appear to have many negatives, there are also some positives. In the article "Code-Dependent: Pros and Cons of the Algorithm Age", authors Lee Raine and Janna Anderson mention that "...everything people see and do on the web is a product of algorithms." They also go on to discuss how algorithms are extremely helpful in automating processes, such as Netflix recommending a movie to watch or the Amazon app recommending products to a user. At this point, algorithms help people to manage their everyday tasks and are intertwined in the decisions people make. Algorithms also "...make sense of massive amounts of data" (Anderson, Raine). After listing some positives of algorithms, these authors also return to the idea that algorithms can cause inequalities and will reflect people's biases. Therefore, the article suggests the need for algorithm literacy, transparency, and oversight.

    After reviewing all of this information, I am compelled to begin teaching algorithm literacy in my classes while also teaching about finding credible research sources. Even though I do find algorithms sometimes useful, such as finding accounts on Instagram I'd enjoy following or having products I'd buy advertised to me, I now understand how algorithms can also put people into a box.

Resources 

Bellovin, S. (2019, January 24). Yes, “algorithms” can be biased. here’s why. Ars Technica. https://arstechnica.com/tech-policy/2019/01/yes-algorithms-can-be-biased-heres-why/ 

Conover, A. (2020, January 21). Why search engines aren’t unbiased with Safiya Noble. Earwolf. https://www.earwolf.com/episode/why-search-engines-arent-unbiased-with-safiya-noble/ 

Rainie, L., & Anderson, J. (2017, February 8). Code-dependent: Pros and cons of the algorithm age. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org